This document discusses the development of time series models for short-term load forecasting using stochastic time series analysis. It describes autoregressive (AR), autoregressive moving average (ARMA), and autoregressive integrated moving average (ARIMA) models. The methodology involves an initial model development phase to identify model orders using autocorrelation and partial autocorrelation functions. A parameter tuning phase then estimates model coefficients to minimize forecast error. Developed models can then be tested and used for forecasting in the next phase. The goal is to accurately predict hourly electrical loads in the short term using these time series techniques.
Short-Term Load Forecasting Using ARIMA Model For Karnataka State Electrical ...IJERDJOURNAL
ABSTRACT: Short-term load forecasting is a key issue for reliable and economic operation of power systems. This paper aims to develop short-term electric load forecasting ARIMA Model for Karnataka Electrical Load pattern based on Stochastic Time Series Analysis. The logical and organised procedures for model development using Autocorrelation Function and Partial Autocorrelation Function make ARIMA Model particularly attractive. The methodology involves Initial Model Development Phase, Parameter Estimation Phase and Forecasting Phase. To confirm the effectiveness, the proposed model is developed and tested using the historical data of Karnataka Electrical Load pattern (2016). The forecasting error of ARIMA Model is computed and results have shown favourable forecasting accuracy.
A Novel Technique in Software Engineering for Building Scalable Large Paralle...Eswar Publications
Parallel processing is the only alternative for meeting computational demand of scientific and technological advancement. Yet first few parallelized versions of a large application code- in the present case-a meteorological Global Circulation Model- are not usually optimal or efficient. Large size and complexity of the code cause making changes for efficient parallelization and further validation difficult. The paper presents some novel techniques to enable change of parallelization strategy keeping the correctness of the code under control throughout the modification.
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity
Short-Term Load Forecasting Using ARIMA Model For Karnataka State Electrical ...IJERDJOURNAL
ABSTRACT: Short-term load forecasting is a key issue for reliable and economic operation of power systems. This paper aims to develop short-term electric load forecasting ARIMA Model for Karnataka Electrical Load pattern based on Stochastic Time Series Analysis. The logical and organised procedures for model development using Autocorrelation Function and Partial Autocorrelation Function make ARIMA Model particularly attractive. The methodology involves Initial Model Development Phase, Parameter Estimation Phase and Forecasting Phase. To confirm the effectiveness, the proposed model is developed and tested using the historical data of Karnataka Electrical Load pattern (2016). The forecasting error of ARIMA Model is computed and results have shown favourable forecasting accuracy.
A Novel Technique in Software Engineering for Building Scalable Large Paralle...Eswar Publications
Parallel processing is the only alternative for meeting computational demand of scientific and technological advancement. Yet first few parallelized versions of a large application code- in the present case-a meteorological Global Circulation Model- are not usually optimal or efficient. Large size and complexity of the code cause making changes for efficient parallelization and further validation difficult. The paper presents some novel techniques to enable change of parallelization strategy keeping the correctness of the code under control throughout the modification.
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity
A New Approach for Design of Model Matching Controllers for Time Delay System...IJERA Editor
Modeling of physical systems usually results in complex high order dynamic representation. The simulation and design of controller for higher order system is a difficult problem. Normally the cost and complexity of the controller increases with the system order. Hence it is desirable to approximate these models to reduced order model such that these lower order models preserves all salient features of higher order model. Lower order models simplify the understanding of the original higher order system. Modern controller design methods such as Model Matching Technique, LQG produce controllers of order at least equal to that of the plant, usually higher order. These control laws are may be too complex with regards to practical implementation and simpler designs are then sought. For this purpose, one can either reduce the order the plant model prior to controller design, or reduce the controller in the final stage, or both. In the present work, a controller is designed such that the closed loop system which includes a delay response(s) matches with those of the chosen model with same time delay as close as possible. Based on desired model, a controller(of higher order) is designed using model matching method and is approximated to a lower order one using Approximate Generalized Time Moments (AGTM) / Approximate Generalized Markov Moments (AGMM) matching technique and Optimal Pade Approximation technique. Genetic Algorithm (GA) optimization technique is used to obtain the expansion points one which yields similar response as that of model, minimizing the error between the response of the model and that of designed closed loop system.
Multiple Vehicle Motion Planning: An Infinite Diminsion Newton Optimization M...AJHaeusler
In this invited talk at the LARSyS Summer School 2014, we describe a numerical algorithm for multiple vehicle motion planning that addresses explicitly temporal and spatial specifications, as well as energy-related constraints. As a motivating example, we cite the case where a group of vehicles is tasked to reach a number of target points at the same time (simultaneous arrival problem) and avoid inter-vehicle as well as vehicle/obstacle collision, subject to the constraint that the overall energy required for vehicle motion be minimized.
The methodology adopted builds on a numerical method for solving optimal control problems that is known as the PRojection Operator based Newton method for Trajectory Optimization (PRONTO)—a method that avoids the transcription phase typical in direct methods for numerical optimal control and that employs an infinite dimension Newton method to achieve second order convergence of the trajectory optimization problem.
With the theoretical set-up adopted, the vehicle dynamics are taken explicitly into account at the planning level. Thus, in contrast to some of the planning methods available in the literature, the method proposed allows for the direct incorporation of dynamical constraints imposed by the physical characteristics of the vehicles, motion actuators, and even energy sources (e.g. batteries). Should the problem to be solved be feasible, the method yields energy-optimal trajectories without the need to separate the steps of path planning and trajectory generation, as is customary in many of the motion planning methods described in the literature. Restrictive system properties such as differential flatness are not required.
In this paper, we have described the coordinate (position) estimation of automatic steered car by using kalman filter and prior knowledge of position of car i.e. its state equation. The kalman filter is one of the most widely used method for tracking and estimation due to its simplicity, optimality, tractability and robustness. However, the application to non linear system is difficult but in extended kalman filter we make it easy as we first linearize the system so that kalman filter can be applied. Kalman has been designed to integrate map matching and GPS system which is used in automatic vehicle location system and very useful tool in navigation. It takes errors or uncertainties via covariance matrix and then implemented to nullify those uncertainties. This paper reviews the motivation, development, use, and implications of the Kalman Filter.
M.G.Goman, A.V.Khramtsovsky (2008) - Computational framework for investigatio...Project KRIT
М.Г.Гоман, А.В.Храмцовский «Методика численного исследования нелинейной динамики самолёта», Advances in Engineering Software 39 (2008), стр..167-177
M.G.Goman, A.V.Khramtsovsky "Computational framework for investigation of aircraft nonlinear dynamics", Advances in Engineering Software 39 (2008) pp.167-177
A computational framework based on qualitative theory, parameter continuation and bifurcation analysis is outlined and illustrated by a number of examples for inertia coupled roll maneuvers. The focus is on the accumulation of computed results in a special database and its incorporation into the investigation process. Ways to automate the investigation of aircraft nonlinear dynamics are considered.
Описана и проиллюстрирована на ряде примеров (связанных с инерционным вращением) методика численного анализа, основанная на теории качественного анализа, методе продолжения решений, зависящих от параметра и на бифуркационном анализе. Особое внимание уделяется накоплению результатов расчетов в специальной базе данных и интеграции этой базы в процесс исследований. Рассмотрены способы автоматизации исследований нелинейной динамики самолёта.
Keep Calm and React with Foresight: Strategies for Low-Latency and Energy-Eff...Tiziano De Matteis
This talk has been given at PPoPP 2016 (Barcelona)
The paper addresses the problem of designing control strategies for elastic stream processing applications. Elasticity allows applications to rapidly change their configuration (e.g. the number of used resources) on-the-fly, in response to fluctuations of their workload. In this work we face this problem by adopting the Model Predictive Control technique, a control-theoretic method aimed at finding the optimal application configuration along a limited prediction horizon by solving an online optimization problem. Our control strategies are designed to address latency constraints, by using Queueing Theory models, and energy consumption by changing the number of used cores and the CPU frequency through the Dynamic Voltage and Frequency Scaling (DVFS) function of modern multi-core CPUs. The proactive capabilities, in addition to the latency- and energy-awareness, represent the novel features of our approach. Experiments performed using a high-frequency trading application show the effectiveness compared with state-of-the-art techniques.
A full version of the slides (with transitions) is available at: https://docs.google.com/presentation/d/1VZ3y3RQDLFi_xA7Rl0Vj1iqBdoerxCMG4y53uMz9Ziw/edit?usp=sharing
ARCH/GARCH model.ARCH/GARCH is a method to measure the volatility of the series, to model the noise term of ARIMA model. ARCH/GARCH incorporates new information and analyze the series based on the conditional variance where users can forecast future values with updated information. Here we used ARIMA-ARCH model to forecast moments. And forecast error 0.9%
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
A New Approach for Design of Model Matching Controllers for Time Delay System...IJERA Editor
Modeling of physical systems usually results in complex high order dynamic representation. The simulation and design of controller for higher order system is a difficult problem. Normally the cost and complexity of the controller increases with the system order. Hence it is desirable to approximate these models to reduced order model such that these lower order models preserves all salient features of higher order model. Lower order models simplify the understanding of the original higher order system. Modern controller design methods such as Model Matching Technique, LQG produce controllers of order at least equal to that of the plant, usually higher order. These control laws are may be too complex with regards to practical implementation and simpler designs are then sought. For this purpose, one can either reduce the order the plant model prior to controller design, or reduce the controller in the final stage, or both. In the present work, a controller is designed such that the closed loop system which includes a delay response(s) matches with those of the chosen model with same time delay as close as possible. Based on desired model, a controller(of higher order) is designed using model matching method and is approximated to a lower order one using Approximate Generalized Time Moments (AGTM) / Approximate Generalized Markov Moments (AGMM) matching technique and Optimal Pade Approximation technique. Genetic Algorithm (GA) optimization technique is used to obtain the expansion points one which yields similar response as that of model, minimizing the error between the response of the model and that of designed closed loop system.
Multiple Vehicle Motion Planning: An Infinite Diminsion Newton Optimization M...AJHaeusler
In this invited talk at the LARSyS Summer School 2014, we describe a numerical algorithm for multiple vehicle motion planning that addresses explicitly temporal and spatial specifications, as well as energy-related constraints. As a motivating example, we cite the case where a group of vehicles is tasked to reach a number of target points at the same time (simultaneous arrival problem) and avoid inter-vehicle as well as vehicle/obstacle collision, subject to the constraint that the overall energy required for vehicle motion be minimized.
The methodology adopted builds on a numerical method for solving optimal control problems that is known as the PRojection Operator based Newton method for Trajectory Optimization (PRONTO)—a method that avoids the transcription phase typical in direct methods for numerical optimal control and that employs an infinite dimension Newton method to achieve second order convergence of the trajectory optimization problem.
With the theoretical set-up adopted, the vehicle dynamics are taken explicitly into account at the planning level. Thus, in contrast to some of the planning methods available in the literature, the method proposed allows for the direct incorporation of dynamical constraints imposed by the physical characteristics of the vehicles, motion actuators, and even energy sources (e.g. batteries). Should the problem to be solved be feasible, the method yields energy-optimal trajectories without the need to separate the steps of path planning and trajectory generation, as is customary in many of the motion planning methods described in the literature. Restrictive system properties such as differential flatness are not required.
In this paper, we have described the coordinate (position) estimation of automatic steered car by using kalman filter and prior knowledge of position of car i.e. its state equation. The kalman filter is one of the most widely used method for tracking and estimation due to its simplicity, optimality, tractability and robustness. However, the application to non linear system is difficult but in extended kalman filter we make it easy as we first linearize the system so that kalman filter can be applied. Kalman has been designed to integrate map matching and GPS system which is used in automatic vehicle location system and very useful tool in navigation. It takes errors or uncertainties via covariance matrix and then implemented to nullify those uncertainties. This paper reviews the motivation, development, use, and implications of the Kalman Filter.
M.G.Goman, A.V.Khramtsovsky (2008) - Computational framework for investigatio...Project KRIT
М.Г.Гоман, А.В.Храмцовский «Методика численного исследования нелинейной динамики самолёта», Advances in Engineering Software 39 (2008), стр..167-177
M.G.Goman, A.V.Khramtsovsky "Computational framework for investigation of aircraft nonlinear dynamics", Advances in Engineering Software 39 (2008) pp.167-177
A computational framework based on qualitative theory, parameter continuation and bifurcation analysis is outlined and illustrated by a number of examples for inertia coupled roll maneuvers. The focus is on the accumulation of computed results in a special database and its incorporation into the investigation process. Ways to automate the investigation of aircraft nonlinear dynamics are considered.
Описана и проиллюстрирована на ряде примеров (связанных с инерционным вращением) методика численного анализа, основанная на теории качественного анализа, методе продолжения решений, зависящих от параметра и на бифуркационном анализе. Особое внимание уделяется накоплению результатов расчетов в специальной базе данных и интеграции этой базы в процесс исследований. Рассмотрены способы автоматизации исследований нелинейной динамики самолёта.
Keep Calm and React with Foresight: Strategies for Low-Latency and Energy-Eff...Tiziano De Matteis
This talk has been given at PPoPP 2016 (Barcelona)
The paper addresses the problem of designing control strategies for elastic stream processing applications. Elasticity allows applications to rapidly change their configuration (e.g. the number of used resources) on-the-fly, in response to fluctuations of their workload. In this work we face this problem by adopting the Model Predictive Control technique, a control-theoretic method aimed at finding the optimal application configuration along a limited prediction horizon by solving an online optimization problem. Our control strategies are designed to address latency constraints, by using Queueing Theory models, and energy consumption by changing the number of used cores and the CPU frequency through the Dynamic Voltage and Frequency Scaling (DVFS) function of modern multi-core CPUs. The proactive capabilities, in addition to the latency- and energy-awareness, represent the novel features of our approach. Experiments performed using a high-frequency trading application show the effectiveness compared with state-of-the-art techniques.
A full version of the slides (with transitions) is available at: https://docs.google.com/presentation/d/1VZ3y3RQDLFi_xA7Rl0Vj1iqBdoerxCMG4y53uMz9Ziw/edit?usp=sharing
ARCH/GARCH model.ARCH/GARCH is a method to measure the volatility of the series, to model the noise term of ARIMA model. ARCH/GARCH incorporates new information and analyze the series based on the conditional variance where users can forecast future values with updated information. Here we used ARIMA-ARCH model to forecast moments. And forecast error 0.9%
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
On selection of periodic kernels parameters in time series predictioncsandit
In the paper the analysis of the periodic kernels parameters is described. Periodic kernels can
be used for the prediction task, performed as the typical regression problem. On the basis of the
Periodic Kernel Estimator (PerKE) the prediction of real time series is performed. As periodic
kernels require the setting of their parameters it is necessary to analyse their influence on the
prediction quality. This paper describes an easy methodology of finding values of parameters of
periodic kernels. It is based on grid search. Two different error measures are taken into
consideration as the prediction qualities but lead to comparable results. The methodology was
tested on benchmark and real datasets and proved to give satisfactory results.
On Selection of Periodic Kernels Parameters in Time Series Prediction cscpconf
In the paper the analysis of the periodic kernels parameters is described. Periodic kernels can
be used for the prediction task, performed as the typical regression problem. On the basis of the
Periodic Kernel Estimator (PerKE) the prediction of real time series is performed. As periodic
kernels require the setting of their parameters it is necessary to analyse their influence on the
prediction quality. This paper describes an easy methodology of finding values of parameters of
periodic kernels. It is based on grid search. Two different error measures are taken into
consideration as the prediction qualities but lead to comparable results. The methodology was
tested on benchmark and real datasets and proved to give satisfactory results.
ON SELECTION OF PERIODIC KERNELS PARAMETERS IN TIME SERIES PREDICTIONcscpconf
In the paper the analysis of the periodic kernels parameters is described. Periodic kernels can
be used for the prediction task, performed as the typical regression problem. On the basis of the
Periodic Kernel Estimator (PerKE) the prediction of real time series is performed. As periodic
kernels require the setting of their parameters it is necessary to analyse their influence on the
prediction quality. This paper describes an easy methodology of finding values of parameters of
periodic kernels. It is based on grid search. Two different error measures are taken into
consideration as the prediction qualities but lead to comparable results. The methodology was
tested on benchmark and real datasets and proved to give satisfactory results.
MFBLP Method Forecast for Regional Load Demand SystemCSCJournals
Load forecast plays an important role in planning and operation of a power system. The accuracy of the forecast value is necessary for economically efficient operation and also for effective control. This paper describes a method of modified forward backward linear predictor (MFBLP) for solving the regional load demand of New South Wales (NSW), Australia. The method is designed and simulated based on the actual load data of New South Wales, Australia. The accuracy of discussed method is obtained and comparison with previous methods is also reported.
Different Models Used In Time Series - InsideAIMLVijaySharma802
We were working for the project Godrej Nature’s Basket, trying to manage its supply chain and delivery partners and would like to accurately forecast the sales for the period starting from “1st January 2019 to 15th January 2019”
Checkout for more articles: https://insideaiml.com/articles
Investigation of Parameter Behaviors in Stationarity of Autoregressive and Mo...BRNSS Publication Hub
The most important assumption about time series and econometrics data is stationarity. Therefore, this study focuses on behaviors of some parameters in stationarity of autoregressive (AR) and moving average (MA) models. Simulation studies were conducted using R statistical software to investigate the parameter values at different orders (p) of AR and (q) of MA models, and different sample sizes. The stationary status of the p and q are, respectively, determined, parameters such as mean, variance, autocorrelation function (ACF), and partial autocorrelation function (PACF) were determined. The study concluded that the absolute values of ACF and PACF of AR and MA models increase as the parameter values increase but decrease with increase of their orders which as a result, tends to zero at higher lag orders. This is clearly observed in large sample size (n = 300). However, their values decline as sample size increases when compared by orders across the sample sizes. Furthermore, it was observed that the means values of the AR and MA models of first order increased with increased in parameter but decreased when sample sizes were decreased, which tend to zero at large sample sizes, so also the variances
A High Order Continuation Based On Time Power Series Expansion And Time Ratio...IJRES Journal
In this paper, we propose a high order continuation based on time power series expansion and time rational representation called Pad´e approximants for solving nonlinear structural dynamic problems. The solution of the discretized nonlinear structural dynamic problems, by finite elements method, is sought in the form of a power series expansion with respect to time. The Pad´e approximants technique is introduced to improve the validity range of power series expansion. The whole solution is built branch by branch using the continuation method. To illustrate the performance of this proposed high order continuation, we give some numerical comparisons on an example of forced nonlinear vibration of an elastic beam.
A Combination of Wavelet Artificial Neural Networks Integrated with Bootstrap...IJERA Editor
In this paper, an iterative forecasting methodology for time series prediction that integrates wavelet de-noising
and decomposition with an Artificial Neural Network (ANN) and Bootstrap methods is put forward here.
Basically, a given time series to be forecasted is initially decomposed into trend and noise (wavelet) components
by using a wavelet de-noising algorithm. Both trend and noise components are then further decomposed by
means of a wavelet decomposition method producing orthonormal Wavelet Components (WCs) for each one.
Each WC is separately modelled through an ANN in order to provide both in-sample and out-of-sample
forecasts. At each time t, the respective forecasts of the WCs of the trend and noise components are simply
added to produce the in-sample and out-of-sample forecasts of the underlying time series. Finally, out-of-sample
predictive densities are empirically simulated by the Bootstrap sampler and the confidence intervals are then
yielded, considering some level of credibility. The proposed methodology, when applied to the well-known
Canadian lynx data that exhibit non-linearity and non-Gaussian properties, has outperformed other methods
traditionally used to forecast it.
A Novel Method for Prevention of Bandwidth Distributed Denial of Service AttacksIJERD Editor
Distributed Denial of Service (DDoS) Attacks became a massive threat to the Internet. Traditional
Architecture of internet is vulnerable to the attacks like DDoS. Attacker primarily acquire his army of Zombies,
then that army will be instructed by the Attacker that when to start an attack and on whom the attack should be
done. In this paper, different techniques which are used to perform DDoS Attacks, Tools that were used to
perform Attacks and Countermeasures in order to detect the attackers and eliminate the Bandwidth Distributed
Denial of Service attacks (B-DDoS) are reviewed. DDoS Attacks were done by using various Flooding
techniques which are used in DDoS attack.
The main purpose of this paper is to design an architecture which can reduce the Bandwidth
Distributed Denial of service Attack and make the victim site or server available for the normal users by
eliminating the zombie machines. Our Primary focus of this paper is to dispute how normal machines are
turning into zombies (Bots), how attack is been initiated, DDoS attack procedure and how an organization can
save their server from being a DDoS victim. In order to present this we implemented a simulated environment
with Cisco switches, Routers, Firewall, some virtual machines and some Attack tools to display a real DDoS
attack. By using Time scheduling, Resource Limiting, System log, Access Control List and some Modular
policy Framework we stopped the attack and identified the Attacker (Bot) machines
Hearing loss is one of the most common human impairments. It is estimated that by year 2015 more
than 700 million people will suffer mild deafness. Most can be helped by hearing aid devices depending on the
severity of their hearing loss. This paper describes the implementation and characterization details of a dual
channel transmitter front end (TFE) for digital hearing aid (DHA) applications that use novel micro
electromechanical- systems (MEMS) audio transducers and ultra-low power-scalable analog-to-digital
converters (ADCs), which enable a very-low form factor, energy-efficient implementation for next-generation
DHA. The contribution of the design is the implementation of the dual channel MEMS microphones and powerscalable
ADC system.
Influence of tensile behaviour of slab on the structural Behaviour of shear c...IJERD Editor
-A composite beam is composed of a steel beam and a slab connected by means of shear connectors
like studs installed on the top flange of the steel beam to form a structure behaving monolithically. This study
analyzes the effects of the tensile behavior of the slab on the structural behavior of the shear connection like slip
stiffness and maximum shear force in composite beams subjected to hogging moment. The results show that the
shear studs located in the crack-concentration zones due to large hogging moments sustain significantly smaller
shear force and slip stiffness than the other zones. Moreover, the reduction of the slip stiffness in the shear
connection appears also to be closely related to the change in the tensile strain of rebar according to the increase
of the load. Further experimental and analytical studies shall be conducted considering variables such as the
reinforcement ratio and the arrangement of shear connectors to achieve efficient design of the shear connection
in composite beams subjected to hogging moment.
Gold prospecting using Remote Sensing ‘A case study of Sudan’IJERD Editor
Gold has been extracted from northeast Africa for more than 5000 years, and this may be the first
place where the metal was extracted. The Arabian-Nubian Shield (ANS) is an exposure of Precambrian
crystalline rocks on the flanks of the Red Sea. The crystalline rocks are mostly Neoproterozoic in age. ANS
includes the nations of Israel, Jordan. Egypt, Saudi Arabia, Sudan, Eritrea, Ethiopia, Yemen, and Somalia.
Arabian Nubian Shield Consists of juvenile continental crest that formed between 900 550 Ma, when intra
oceanic arc welded together along ophiolite decorated arc. Primary Au mineralization probably developed in
association with the growth of intra oceanic arc and evolution of back arc. Multiple episodes of deformation
have obscured the primary metallogenic setting, but at least some of the deposits preserve evidence that they
originate as sea floor massive sulphide deposits.
The Red Sea Hills Region is a vast span of rugged, harsh and inhospitable sector of the Earth with
inimical moon-like terrain, nevertheless since ancient times it is famed to be an abode of gold and was a major
source of wealth for the Pharaohs of ancient Egypt. The Pharaohs old workings have been periodically
rediscovered through time. Recent endeavours by the Geological Research Authority of Sudan led to the
discovery of a score of occurrences with gold and massive sulphide mineralizations. In the nineties of the
previous century the Geological Research Authority of Sudan (GRAS) in cooperation with BRGM utilized
satellite data of Landsat TM using spectral ratio technique to map possible mineralized zones in the Red Sea
Hills of Sudan. The outcome of the study mapped a gossan type gold mineralization. Band ratio technique was
applied to Arbaat area and a signature of alteration zone was detected. The alteration zones are commonly
associated with mineralization. The alteration zones are commonly associated with mineralization. A filed check
confirmed the existence of stock work of gold bearing quartz in the alteration zone. Another type of gold
mineralization that was discovered using remote sensing is the gold associated with metachert in the Atmur
Desert.
Reducing Corrosion Rate by Welding DesignIJERD Editor
The paper addresses the importance of welding design to prevent corrosion at steel. Welding is
used to join pipe, profiles at bridges, spindle, and a lot more part of engineering construction. The
problems happened associated with welding are common issues in these fields, especially corrosion.
Corrosion can be reduced with many methods, they are painting, controlling humidity, and also good
welding design. In the research, it can be found that reducing residual stress on the welding can be
solved in corrosion rate reduction problem.
Preheating on 500oC and 600oC give better condition to reduce corosion rate than condition after
preheating 400oC. For all welding groove type, material with 500oC and 600oC preheating after 14 days
corrosion test is 0,5%-0,69% lost. Material with 400oC preheating after 14 days corrosion test is 0,57%-0,76%
lost.
Welding groove also influence corrosion rate. X and V type welding groove give better condition to reduce
corrosion rate than use 1/2V and 1/2 X welding groove. After 14 days corrosion test, the samples with
X welding groove type is 0,5%-0,57% lost. The samples with V welding groove after 14 days corrosion test is
0,51%-0,59% lost. The samples with 1/2V and 1/2X welding groove after 14 days corrosion test is 0,58%-
0,71% lost.
Router 1X3 – RTL Design and VerificationIJERD Editor
Routing is the process of moving a packet of data from source to destination and enables messages
to pass from one computer to another and eventually reach the target machine. A router is a networking device
that forwards data packets between computer networks. It is connected to two or more data lines from different
networks (as opposed to a network switch, which connects data lines from one single network). This paper,
mainly emphasizes upon the study of router device, it‟s top level architecture, and how various sub-modules of
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Active Power Exchange in Distributed Power-Flow Controller (DPFC) At Third Ha...IJERD Editor
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transmission lines at the third-harmonic frequency. DPFC multiple small-size single-phase converters which
reduces the cost of equipment, no voltage isolation between phases, increases redundancy and there by
reliability increases. The principle and analysis of the DPFC are presented in this paper and the corresponding
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voltage, current and frequency. Electronic devices are very sensitive loads. In power system voltage sag,
swell, flicker and harmonics are some of the problem to the sensitive load. The compensation capability
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Study on the Fused Deposition Modelling In Additive ManufacturingIJERD Editor
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Spyware triggering system by particular string valueIJERD Editor
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A Blind Steganalysis on JPEG Gray Level Image Based on Statistical Features a...IJERD Editor
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Secure Image Transmission for Cloud Storage System Using Hybrid SchemeIJERD Editor
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Application of Buckley-Leverett Equation in Modeling the Radius of Invasion i...IJERD Editor
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Gesture Gaming on the World Wide Web Using an Ordinary Web CameraIJERD Editor
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Hardware Analysis of Resonant Frequency Converter Using Isolated Circuits And...IJERD Editor
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unknown characteristic of this converter it was used as a series resonant converter with basically a passive
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waveform .The basic goal of this particular analysis is to observe the wave forms and characteristics of
converters with differently positioned passive elements in the form of tank circuits.
Simulated Analysis of Resonant Frequency Converter Using Different Tank Circu...IJERD Editor
LLC resonant frequency converter is basically a combo of series as well as parallel resonant ckt. For
LCC resonant converter it is associated with a disadvantage that, though it has two resonant frequencies, the
lower resonant frequency is in ZCS region [5]. For this application, we are not able to design the converter
working at this resonant frequency. LLC resonant converter existed for a very long time but because of
unknown characteristic of this converter it was used as a series resonant converter with basically a passive
(resistive) load. . Here, it was designed to operate in switching frequency higher than resonant frequency of the
series resonant tank of Lr and Cr converter acts very similar to Series Resonant Converter. The benefit of LLC
resonant converter is narrow switching frequency range with light load[6] . Basically, the control ckt plays a
very imp. role and hence 555 Timer used here provides a perfect square wave as the control ckt provides no
slew rate which makes the square wave really strong and impenetrable. The dead band circuit provides the
exclusive dead band in micro seconds so as to avoid the simultaneous firing of two pairs of IGBT’s where one
pair switches off and the other on for a slightest period of time. Hence, the isolator ckt here is associated with
each and every ckt used because it acts as a driver and an isolation to each of the IGBT is provided with one
exclusive transformer supply[3]. The IGBT’s are fired using the appropriate signal using the previous boards
and hence at last a high frequency rectifier ckt with a filtering capacitor is used to get an exact dc
waveform .The basic goal of this particular analysis is to observe the wave forms and characteristics of
converters with differently positioned passive elements in the form of tank circuits. The supported simulation
is done through PSIM 6.0 software tool
Amateurs Radio operator, also known as HAM communicates with other HAMs through Radio
waves. Wireless communication in which Moon is used as natural satellite is called Moon-bounce or EME
(Earth -Moon-Earth) technique. Long distance communication (DXing) using Very High Frequency (VHF)
operated amateur HAM radio was difficult. Even with the modest setup having good transceiver, power
amplifier and high gain antenna with high directivity, VHF DXing is possible. Generally 2X11 YAGI antenna
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visibility of Moon at both the stations and other vital data to acquire real time position of moon.
“MS-Extractor: An Innovative Approach to Extract Microsatellites on „Y‟ Chrom...IJERD Editor
Simple Sequence Repeats (SSR), also known as Microsatellites, have been extensively used as
molecular markers due to their abundance and high degree of polymorphism. The nucleotide sequences of
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crucial. However, Microsatellites repeat count is compared, if they differ largely, he has some disorder. The Y
chromosome likely contains 50 to 60 genes that provide instructions for making proteins. Because only males
have the Y chromosome, the genes on this chromosome tend to be involved in male sex determination and
development. Several Microsatellite Extractors exist and they fail to extract microsatellites on large data sets of
giga bytes and tera bytes in size. The proposed tool “MS-Extractor: An Innovative Approach to extract
Microsatellites on „Y‟ Chromosome” can extract both Perfect as well as Imperfect Microsatellites from large
data sets of human genome „Y‟. The proposed system uses string matching with sliding window approach to
locate Microsatellites and extracts them.
Importance of Measurements in Smart GridIJERD Editor
- The need to get reliable supply, independence from fossil fuels, and capability to provide clean
energy at a fixed and lower cost, the existing power grid structure is transforming into Smart Grid. The
development of a smart energy distribution grid is a current goal of many nations. A Smart Grid should have
new capabilities such as self-healing, high reliability, energy management, and real-time pricing. This new era
of smart future grid will lead to major changes in existing technologies at generation, transmission and
distribution levels. The incorporation of renewable energy resources and distribution generators in the existing
grid will increase the complexity, optimization problems and instability of the system. This will lead to a
paradigm shift in the instrumentation and control requirements for Smart Grids for high quality, stable and
reliable electricity supply of power. The monitoring of the grid system state and stability relies on the
availability of reliable measurement of data. In this paper the measurement areas that highlight new
measurement challenges, development of the Smart Meters and the critical parameters of electric energy to be
monitored for improving the reliability of power systems has been discussed.
Study of Macro level Properties of SCC using GGBS and Lime stone powderIJERD Editor
One of the major environmental concerns is the disposal of the waste materials and utilization of
industrial by products. Lime stone quarries will produce millions of tons waste dust powder every year. Having
considerable high degree of fineness in comparision to cement this material may be utilized as a partial
replacement to cement. For this purpose an experiment is conducted to investigate the possibility of using lime
stone powder in the production of SCC with combined use GGBS and how it affects the fresh and mechanical
properties of SCC. First SCC is made by replacing cement with GGBS in percentages like 10, 20, 30, 40, 50 and
by taking the optimum mix with GGBS lime stone powder is blended to mix in percentages like 5, 10, 15, 20 as
a partial replacement to cement. Test results shows that the SCC mix with combination of 30% GGBS and 15%
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State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
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State of global ICS asset and network exposure
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Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
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In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
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Cyber risk predictions
Axis of attacks – Europe
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Download the full report from here:
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JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
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Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
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- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
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- Demonstration of InfluxDB and Grafana using a practice web application
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In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
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International Journal of Engineering Research and Development
1. International Journal of Engineering Research and Development
e-ISSN: 2278-067X, p-ISSN: 2278-800X, www.ijerd.com
Volume 9, Issue 11 (February 2014), PP. 31-36
31
Study & Development of Short Term Load Forecasting Models
Using Stochastic Time Series Analysis
V.Venkatesh1
, Shilpa G N2
,Nataraja.C3
1
Professor, ECE Department & Principal, C.I.T., Gubbi, Tumkur, Karnataka, India
2
Assistant professor, E&EE Department, SSIT, Tumkur, Karnataka, India.
3
Engineer, MTech(Energy System Engineering) , Tumkur, Karnataka, India.
Abstract:- The present paper involves the study & development of various time series models for Short Term
Electrical Load Forecasting Using Time series approach. Given one year load data, first six months data is used
for model development and then these models can be tested using next six months data. Different models for
Short term load forecasting using time series approach such as Autoregressive (AR) models, Autoregressive
Moving Average (ARMA) models, Autoregressive Integrated Moving Average (ARIMA) models and are
developed. The methodology involves Initial Model Development Phase, Parameter Tuning Phase and
Forecasting Phase.
Index Terms:- Autoregressive Moving Average (ARMA), Autoregressive Integrated Moving Average
(ARIMA), model. Autocorrelation function (acf), autocorrelation function (pacf).
I. INTRODUCTION
Load forecasting has always been the essential part of an efficient power system planning and
operation.
Power system expansion planning starts with a forecast of anticipated future load requirement.
Estimates of both demand and energy required are crucial to effective system planning. Demand forecasts are
used to determine the capacity of generation, transmission, and distribution system additions and energy
forecasts determine the type of facilities required. Load forecasts are also used to establish procurement policies
for construction capital where for sound operation the balance must be maintained in the use of dept and equity
capital. Further energy forecasts are used to determine future fuel requirement and if necessary when fuel prices
soar rate relief to maintain an adequate rate of return. In summary good forecast reflecting current and future
trends tempered with good judgment is the key to planning indeed to financial success. Short-term load
forecasting activities include forecasting the daily load curve as a series of 24 hourly forecasted loads.
Various techniques for power system load forecasting have been proposed in the last few decades.
Load forecasting with time leads, from a few minutes to several days helps the system operator to efficiently
schedule spinning reverse allocation, can provide information which is able to be used for possible energy
interchange with other utilities. In addition to these economical reasons it is also useful for system security. The
idea of time series approach is based on the understanding that a load pattern is nothing more than a time series
signal with known seasonal, weekly and daily predictions. These predictions give a rough prediction of the load
at the given season, day of the week and time of the day. Time series forecasting methods are based on the
premises that we can predict future performance of a measure simply by analyzing its past results. These
methods identify a pattern in the historical data and use that pattern to extrapolate future values. Past results can,
in fact, be very reliable predictor for a short period into the future.
In this context, the development of an accurate, fast and robust short term load forecasting
methodology is of importance to both the utility and its customers. An attempt has been made for studying Short
Term Hourly Load Forecasting using time series approach by developing Autoregressive (AR), Autoregressive
Moving Average (ARMA), Autoregressive Integrated Moving Average (ARIMA) models.
The power load demand is sensitive to weather variables. The effect of the weather variables such as
Temperature, Humidity, Wind speed and Cloud coverage on the load demand can be considered in the
development of these models for short term load forecasting using time series approach. Also non weather
variables can be taken into consideration. Also while developing these models Holidays and special events can
be separately considered.
II. TIME SERIES MODELS IN LOAD FORECASTING:
This method appears to be the most popular approach that has been applied and is still being applied in
electric power industry for short term load forecasting.
2. Study & Development of Short Term Load Forecasting Models Using Stochastic Time Series Analysis
32
Fig2(a). Load time series modeling
The power system load is assumed to be time dependent evolving according to a probabilistic law. It is
a common practice to employ a white noise sequences a(t) as input to a linear filter whose output y(t) is the
power system load. This is an adequate model for predicting the load time series. The noise input is assumed
normally distributed with zero mean and some variance σt. Time series models can use non weather as well as
weather variables. These models are most widely used for load forecasting.
2.1 The Autoregressive (AR) process:
In the Autoregressive process, the current value of the time series y (t) is expressed linearly in terms of
its „p‟ previous values [y (t-1), y (t-2)……. y (t-p)] and a random noise a (t).
For an autoregressive process of order „p‟ i.e. AR (p), the model can be written as,
y (t) = Ø1 y (t-1) + ……..+ Øp y (t-p) + a (t)
----------- 1
In order to write this in more convenient form the following operators are introduced.
B y (t) = y (t-1);
Bm
y (t) = y (t-m);
And
A (q) = 1- Ø1 B1
– Ø2 B2
- ……………- Øp Bp
;
So equation 1 can be written as,
A (q) y (t) = a (t) ------------ 2
Where,
y (t) – output or the load at time„t‟
B - Backshift operator
A (q) – delay polynomial
Ø1… Øp – coefficients of delay
Polynomial
p – Order of the delay polynomial
a (t) – random noise
2.2 The Moving Average (MA) Process:
In the moving average process, the current value of the time series y (t) is expressed linearly in terms
of current and previous „q‟ values of a white noise series [ a (t), a (t-1)………a(t-q)].The noise series is
constructed from the forecast errors or residuals when load observations become available.
For a moving average of order „q‟ i.e. MA (q), the model can be written as,
y (t) = a (t) + θ1 a (t-1) + ………+ θq a (t-q)
3. Study & Development of Short Term Load Forecasting Models Using Stochastic Time Series Analysis
33
--------- 3
A similar application of backshift operator on white noise series would allow equation 3 to be written
as,
y (t) = C (q) a (t) ------------- 4
And
C (q) = 1+ θ1 B1
+ θ2 B2
+ ………..+ θq Bq
;
Where,
y (t) – output or the load at time„t‟
B - Backshift operator
C (q) – delay polynomial
θ1…..θq - coefficients of delay
polynomial
q – Order of the delay polynomial
a (t) – random noise
2.3 The Autoregressive Moving-Average (ARMA) Process:
In the autoregressive moving average process, the current value of the time series y (t) is expressed
linearly in terms of its previous „p‟ values [y (t-1), y (t-2)……..y (t-p) ] and in terms of current and previous
„q‟ values of a white noise [a (t), a (t-1).…...a (t-q) ].
For an autoregressive moving average process of order „p‟ and „q‟ i.e. ARMA (p, q), the model is
written as,
y (t) = Ø1 y (t-1) + ………..+ Øp y (t-p) + a (t)
+θ1a(t-1)+…….+θqa(t-q)
------------------ 5
By using the backshift operator defined earlier equation 5 can be written as,
A (q) y (t) = C (q) a (t) ------------- 6
Where,
A (q) & C (q) – delay polynomials
p & q – Orders of the delay polynomials
A (q) & C (q) respectively.
2.4 The Autoregressive Integrated Moving-Average (ARIMA) Process:
The time series defined previously as an AR, MA or as an ARMA process is called a stationary
process. This means that the mean of the series of any of these processes and the covariances among its
observations do not change with time. If the process is non-stationary, transformation of the series to a
stationary process has to be performed first. This can be achieved, for the time series that are non-stationary in
mean, by a differencing process.
By introducing the ▼ operator, a differenced time series of order 1 can be written as,
▼y (t) = y (t) – y (t-1) = (1-B) y (t); using the definition of backshift operator, B. Consequently, an
order „d‟ differenced time series is written as,
▼d
y (t) = (1-B) d
y (t);
The differenced stationary series can be modeled as an AR, MA, or an ARMA to yield an ARIMA time
series processes.
For a series that needs to be differenced „d‟ times and has the orders „p‟ and „q‟ for AR and MA
components i.e. ARIMA (p,d,q) model is written as,
A (q) ▼d
y (t) = C (q) a (t)
----------------- 7
Where A (q) , ▼d
, and C (q) have been defined earlier.
4. Study & Development of Short Term Load Forecasting Models Using Stochastic Time Series Analysis
34
III. MAIN GOALS OF TIME SERIES ANALYSIS:
There are two main goals of time series analysis:
Identifying the nature of the phenomenon represented by the sequence of observations
Forecasting or predicting the future values of the time series.
Both of these goals require that the pattern of the observed time series data is identified and more or
less formally described. Once the pattern is established, we can interpret and integrate it with other data. In time
series analysis it is assumed that the data consists of a systematic pattern and a random noise which usually
makes the pattern difficult to identify. Most time series analysis techniques involve some form of filtering out
noise in order to make the pattern more salient.
IV. TWO GENERAL ASPECTS OF TIME SERIES PATTERNS
Most time series patterns can be described in terms of two basic classes of components:
Trend
Seasonality
The former represents a general systematic linear or (most often) nonlinear component that changes
over time and does not repeat or at least does not repeat within the time range captured by our data. The latter
may have formally similar nature; however it repeats itself in systematic intervals over time.
There are no proven “automatic” techniques to identify trend components in the time series data:
however, as long as the trend is monotonous (consistently increasing or decreasing) that part of data analysis is
typically not very difficult. If the time series data contain considerable error, then the first step in the process of
trend identification is smoothing. Smoothing always involves some form of local averaging of data such that
nonsystematic components of individual observations cancel each other out.
Seasonal dependency (seasonality) is another general component of the time series pattern. It is
formally defined as correlation dependency of order „k‟ between each „ith‟
element of the series and the (i-k)th
element and measured by autocorrelation :„k‟ is usually called the lag. If the measurement error is not too large,
seasonality can be visually identified in the series as a pattern that repeats every „k‟ elements.
4.1 Autocorrelation Function (Acf):
Autocorrelation is a mathematical tool used for analyzing functions or series of values. Informally, it
the measure of how well a signal matches a time-shifted version of itself, as a function of the amount of time
shift. Autocorrelation is useful in finding repeating patterns in a signal. The autocorrelation function describes
inherent correlation between observations of a time series which are separated in time by some lag „k‟.
It is given by,
ρk = Ø1 ρk-1 + ……………+ Øp ρk-p ;
Where,
ρk = γk / γo ;
γk = E [ y (t) y (t+k) ] ;
If the function ρ is well defined its value must lie in the range [-1 1], with 1 indicating perfect
correlation and -1 indicating perfect anticorrelation.
Seasonal patterns of time series can be examined via correlograms. The Autocorrelation correlograms
displays graphically and numerically the autocorrelation function, i.e. serial correlation coefficients for
consecutive lags in a specified range of lags.
4.2 Partial Autocorrelation Function (Pacf):
Another useful method to examine serial dependencies is to examine the partial autocorrelation
function. Here correlations with all the elements within the lag are partialled out. If the lag of 1 is specified ( i.e.
there are no intermediate elements within the lag), then the partial autocorrelation is equivalent to
autocorrelation. In the sense, the partial autocorrelation provides a clearer picture of serial dependencies for
individual lags.
Serial dependency for a particular lag of „k‟ can be removed by differencing the series, i.e. converting
each ith
element of the series into its difference from the (i-k)th
element. There are two major reasons for such
transformations. First; we can identify the hidden nature of seasonal dependencies in the series. As mentioned
earlier, autocorrelations for consecutive lags are interdependent. Therefore, removing some of the
autocorrelations will change other autocorrelations and it may eliminate them or it may make some other
seasonalities apparent. The other reason for removing seasonal dependencies is to make the time series
stationary which is necessary for ARIMA model.
5. Study & Development of Short Term Load Forecasting Models Using Stochastic Time Series Analysis
35
Hence techniques for preliminary identification of time series models rely on the analysis of
autocorrelation and partial autocorrelation function. These methods are very systematic and are extremely
helpful in the determination of model order, in preliminary estimation of model parameters and model
refinement.
V. MODEL DEVELOPMENT
To implement the proposed methodology, a statistical study of load demand has to be carried out for
short term load forecasting. This statistical study includes daily hourly loads for one year. The Autoregressive
(AR), Autoregressive Moving Average (ARMA) and Autoregressive Integrated Moving Average (ARIMA)
models can be developed using time series approach for short term load forecasting on first six months data and
these models are used for forecasting on the next six months data in order to provide comparisons with the
forecasts.
Development of these models comprises of three major computational steps:
Initial model development phase
Parameter tuning phase
Forecasting phase
In initial model development phase techniques for preliminary identification of time series models rely
on the analysis of the autocorrelation function (acf) and partial autocorrelation function (pacf).These methods
are very systematic and are extremely helpful in the determination of model order, preliminary estimation of
model parameters, diagnostic checking and model refinement. For an Autoregressive process, partial
autocorrelation function (pacf) is useful in determination of the order of the AR model & autocorrelation
function (acf) for Moving Average (MA) process is useful in determining the orders of the MA model.
In Parameter tuning phase, all the various proposed models calculates the coefficients of the delay
polynomials using gradient based efficient estimation method i.e. Least Square method so that the energy of the
noise term is minimized. Minimum forecasting error is viewed as the principal criterion in determining both
model orders and its parameters.
Once the parameters of the models have been estimated, they can be substituted in the various model
equations discussed earlier & the adequacy of the model has to be tested known as the diagnostic checking. This
testing procedure is performed so as to check if the parameter estimate is significantly different from zero & if
the models pass the above test, they can be used for forecasting.
VI. CONCLUSION
Hence study of various time series models & model developments are discussed .Hence an attempt has
been successfully made for short term load forecasting using time series approach by studying & by knowing
how to develop Autoregressive (AR), Autoregressive Moving Average (ARMA), Autoregressive Integrated
Moving Average (ARIMA) models.
Three computational steps for time series model development, initial model development phase,
parameter tuning phase & forecasting phase are also discussed. The methodology identifies the proper initial
model orders, proper selection of input variables and involves estimation of model parameters. Then these
models are used to forecast the future hourly load.
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