Short-term Load Forecasting based on Neural network and Local RegressionJie
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The document discusses short-term load forecasting using neural networks and local regression models. It finds that a combination model using local regression followed by a neural network to refine the results provides the best accuracy, achieving an error rate of 2.70% for day-ahead forecasts. This multi-step approach first uses local regression to identify meaningful factors like the influence of temperature, then trains a neural network using these factors to further improve the forecasts.
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![Short-term Load Forecasting based on Neural network and Local Regression Jie Bao a Ellen Maxon b Vasant Honavar c ac Artificial Intelligence Lab, Dept of Computer Science Iowa State University, Ames, IA, 50010 baojie,honavar@cs.iastate.edu b Power Domain, Inc 7575 Palos Verdes Reno, Nevada 89502, ceo@powerdomain.com Abstract : Accurate short term load forecasts (STLF) are of significant financial implication for the utility industry as well as the consumers. We used a multi-stage approach to solving the STLF problem. Initially, when little is known about for the exact form of the load function f , a neural network is used to uncover predictive relationships between input and output. The resulting neural network is examined to identify factors that are most meaningful in STLF. A linear model is constructed using the identified factors leading to a Local Regression model (with Moving Average as a special case). We then explore a Neural Network (NN) model that refines the linear regression model further, with the result from Local Regression (LR) as guidance for the training of the neural network. Best day-ahead result from a combination model of LR+NN is 2.70% error The results demonstrate the usefulness of a multi-step iterative refinement approach to model building in solving complex problems such as STLF . ,[object Object],[object Object],[object Object],[object Object],[object Object],2 What may be the influential factors in STLF Summer winter winter ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Day 1 Day 4 Week 1 Week 5 Temperature and Humidity Normalized average load and temperature (dashed line) for every week Linear regression of weekly average temperature and load Overlapped influence Of calendar and weather Winter Summer Temperature shifting 3 How to do the forecasting ? Find the function f - it is complex and unknown, but really existent and consistent : L f (t) = f(L(t),L(t-1),…L(t-k),c,w,e) f Load History [L] Calendar [c] Weather [w] Others [e] Forecasted Load [L f ] ? 4 Black box model: Neural Network - with little expertise knowledge is known It’s a common and naïve way as treating the problem as a time series forecasting problem, and function approximation. Ensemble network is used to reduce model bias INPUTs: hour , real/forecasted temperature , load history, weekend, weekday OUPUT: load of the future Day-ahead model : Best result 5.45% error Hour-ahead model: Best Result 1.53% ,[object Object],[object Object],[object Object],[object Object],[object Object],5 White box model 1 : Moving Average - with knowing something Long-range influence: L i is load at past reference point, eg. a week ago , w i is its weight - all calendar information is well kept. Short-range influence: Temperature shifting and error feed back is applied Day-ahead model : Best result 3.54% error 6 White box model 2 : Local Regression - with load pattern is somehow clear ,[object Object],[object Object],Lr(n) and k(n) are the constant and linear order coefficients for given time n ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],7 Hybrid model: Neural Network plus Local Regression - there still factors we don’t clear ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Comparison NN: 5.45% MA: 3.54% LR: 3.22% NN+LR: 2.97% NN+LR + no errFeedBack: 2.70% More on http://www.public.iastate.edu/~baojie/pub/2002-10-25_stlf.ppt.pdf ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],… … … … … … Load History Calendar Information Weather Information Forecasted Load](https://image.slidesharecdn.com/2003-10-27poster-100706164304-phpapp01/85/Short-term-Load-Forecasting-based-on-Neural-network-and-Local-RegressionJie-1-320.jpg)
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Electrical engineers involved in power generating plants with utility scale generators
Electrical engineers in large industries who are associated with power distribution
Consulting engineers involved in design of substations
Contractors executing projects involving electrical HV substations
Electrical commissioning engineers
MORE INFORMATION: http://www.idc-online.com/content/electrical-substation-and-switchyard-design-25
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To develop a predictive model to forecast energy load demand based on historical load data.
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Electrical substations form important nodal points in all power networks. Substations can be of various capacities, voltages, configurations and types depending on what is the application for which the substation is being designed. Location and layout of a substation present a number of challenges to the designer due to a large variety of options available to a designer. There are ever so many constraints too that need to be kept in mind; technical, environmental and naturally financial. Arriving at an optimum design within these constraints is as much an art as it is a science. Designing a substation which will operate with utmost reliability for at the least three or four decades involves a thorough knowledge of the current state-of-the art equipment, emerging technologies, the tools for presenting and evaluating all available options and a good appreciation of power system operation and maintenance. This course will present a comprehensive capsule of all the knowledge essential for a substation designer and walk the participants through the substation design process using a set of interlinked case studies.
FOR MORE INFORMATION: http://www.idc-online.com/content/electrical-substation-and-switchyard-design-25
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Short-term Load Forecasting based on Neural network and Local Regression
Short-term Load Forecasting based on Neural network and Local Regression
Artificial neural network for load forecasting in smart grid
Artificial neural network for load forecasting in smart grid
Load types, estimation, grwoth, forecasting and duration curves
Load types, estimation, grwoth, forecasting and duration curves
Similar to Short-term Load Forecasting based on Neural network and Local RegressionJie
Short Term Load Forecasting Using Multi Layer Perceptron
Load forecasting is the method for prediction of Electrical load. Short term load forecasting is
one of the important concerns of power system and accurate load forecasting is essential for managing
supply and demand of electricity. The basic objective of STLF is to predict the near future load for example
next hour load prediction or next day load prediction etc….There are various factors which influence the
behaviour of the consumer load. The factors that we consider in this paper are Load,Temperature, humidity,
time. The ANN is used to learn the relationship among past, current and future parameters like load, temp. In
this paper we are using Multi parameter regression and comparing the results with the Artificial Neural
network output. Finally, outcomes of the approaches are evaluated and compared by means of the Mean
absolute Percentage error (MAPE).ANN outcomes are more fairly
accurate to the actual loads than those of conventional methods. So it can be considered as the suitable tool
to deal with STLF problems.
Short-term load forecasting with using multiple linear regression
In this paper short term load forecasting (STLF) is done with using multiple linear regression (MLR). A day ahead load forecasting is obtained in this paper. Regression coefficients were found out with the help of method of least square estimation. Load in electrical power system is dependent on temperature, due point and seasons and also load has correlation to the previous load consumption (Historical data). So the input variables are temperature, due point, load of prior day, hours, and load of prior week. To validate the model or check the accuracy of the model mean absolute percentage error is used and R squared is checked which is shown in result section. Using day ahead forecasted data weekly forecast is also obtained.
Machine Learning for Weather Forecasts
In this deck from the Stanford HPC Conference, Peter Dueben from the European Centre for Medium-Range Weather Forecasts (ECMWF) presents: Machine Learning for Weather Forecasts.
"I will present recent studies that use deep learning to learn the equations of motion of the atmosphere, to emulate model components of weather forecast models and to enhance usability of weather forecasts. I will than talk about the main challenges for the application of deep learning in cutting-edge weather forecasts and suggest approaches to improve usability in the future."
Peter is contributing to the development and optimization of weather and climate models for modern supercomputers. He is focusing on a better understanding of model error and model uncertainty, on the use of reduced numerical precision that is optimised for a given level of model error, on global cloud- resolving simulations with ECMWF's forecast model, and the use of machine learning, and in particular deep learning, to improve the workflow and predictions. Peter has graduated in Physics and wrote his PhD thesis at the Max Planck Institute for Meteorology in Germany. He worked as Postdoc with Tim Palmer at the University of Oxford and has taken up a position as University Research Fellow of the Royal Society at the European Centre for Medium-Range Weather Forecasts (ECMWF) in 2017.
Watch the video: https://youtu.be/ks3fkRj8Iqc
Learn more: https://www.ecmwf.int/
and
http://www.hpcadvisorycouncil.com/events/2020/stanford-workshop/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Fuzzy logic methodology for short term load forecasting
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Short term load forecasting system based on support vector kernel methods
Load Forecasting is powerful tool to make important decisions such as to purchase and generate the
electric power, load switching, development plans and energy supply according to the demand. The
important factors for forecasting involve short, medium and long term forecasting. Factors in short term
forecasting comprises of whether data, customer classes, working, non-working days and special event
data, while long term forecasting involves historical data, population growth, economic development and
different categories of customers.In this paper we have analyzed the load forecasting data collected from
one grid that contain the load demands for day and night, special events, working and non-working days
and different hours in day. We have analyzed the results using Machine Learning techniques, 10 fold cross
validation and stratified CV. The Machines Learning techniques used are LDA, QDA, SVM Polynomial,
Gaussian, HRBF, MQ kernels as well as LDA and QDA. The errors methods employed against the
techniques are RSE, MSE, RE and MAPE as presented in the table 2 below. The result calculated using the
SVM kernel shows that SVM MQ gives the highest performance of 99.53 %.
Short Presentation: Mohamed abuella's Research Highlights
Short Presentation: Mohamed Abuella's Research Highlights
Comparative study of fuzzy logic and ann for short term load forecasting
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Neural wavelet based hybrid model for short-term load forecasting
International peer-reviewed academic journals call for papers, http://www.iiste.org/Journals
Presentation - Msc Thesis - Machine Learning Techniques for Short-Term Electr...
Presentation - Msc Thesis - Machine Learning Techniques for Short-Term Electr...Praxitelis Nikolaos Kouroupetroglou
This is a master thesis which shows how to apply machine learning models to prediction electricity load demand with short-term (1 day ahead) horizon.A Post-processing Approach for Solar Power Combined Forecasts of Ramp Events
Mohamed Abuella's Dissertation at University of North Carolina at Charlotte
CFD down-scaling and online measurements for short-term wind power forecasting
Usually speaking, Forecast systems are classified : Intraday (Very Short term) is commonly Stochastic with online measurements while Extraday (Short term) is usually Deterministic based on NWP data. This work aims to breakdown these classifications, proposing a unique tool based on the unification of all these techniques.
MFBLP Method Forecast for Regional Load Demand System
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.
INTERVAL TYPE-2 FUZZY NEURAL NETWORKS FOR SHORT-TERM ELECTRIC LOAD FORECASTIN...
This paper focuses on the study of short term load forecasting (STELF) using interval Type-2 Fuzzy Logic (IT2FL) and feed-forward Neural Network with back-propagation (NN-BP) tuning algorithm to improve their approximation capability, flexibility and adaptiveness. IT2FL for STELF is presented which provides
additional degrees of freedom for handling more uncertainties for improving prediction accuracy and reducing cost. The IT2FL comprises five components which include; the fuzzification unit, the knowledge base, the inference engine, the type reducer and the defuzzification unit. Gaussian membership function is
used to show the degree of membership of the input variables. The lower and upper membership functions (fired rules) as well as their consequent coefficients of IT2FL are fed into a (NN) which produces a crisp value coresponding to the optimal defuzzified output of IT2FLSs. The NN type reducer is trained to
optimize parameters of membership function (MF) so as to produce an output with minimum error function with the purpose of improving forecasting performance of IT2FLS models. The IT2FNN system has the ability to overcome the limitations of individual technique and enhances their strengths to handle electric load forecasting. The IT2FNN is applied for STELF in Akwa Ibom State-Nigeria. The result of performance
of IT2FNN is compared with IT2FLS and T1FLS methods for short term load forecasting with MSE of 0.00123, 0.00185 and 0.00247 respectively. Also, the results of forecasting are compared using RMSE of 0.035, 0.043 and 0.035 respectively, indicating a best accurate forecasting with IT2FNN. In addition, the result of performance of IT2FNN is compared with IT2FLS and T1FLS methods for short term load forecasting with MAPE of 1.5%, 3% and 4.5% respectively. Simulation results show that the IT2FNN approach takes advantages of accuracy and efficiency and performs better in prediction than IT2FL and
T1FL methods in power load forecasting task. .
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Short Term Load Forecasting Using Multi Layer Perceptron
Short Term Load Forecasting Using Multi Layer Perceptron
Short-term load forecasting with using multiple linear regression
Short-term load forecasting with using multiple linear regression
Fuzzy logic methodology for short term load forecasting
Fuzzy logic methodology for short term load forecasting
Short term load forecasting system based on support vector kernel methods
Short term load forecasting system based on support vector kernel methods
Short Presentation: Mohamed abuella's Research Highlights
Short Presentation: Mohamed abuella's Research Highlights
Comparative study of fuzzy logic and ann for short term load forecasting
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Presentation - Msc Thesis - Machine Learning Techniques for Short-Term Electr...
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A Post-processing Approach for Solar Power Combined Forecasts of Ramp Events
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CFD down-scaling and online measurements for short-term wind power forecasting
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MFBLP Method Forecast for Regional Load Demand System
MFBLP Method Forecast for Regional Load Demand System
INTERVAL TYPE-2 FUZZY NEURAL NETWORKS FOR SHORT-TERM ELECTRIC LOAD FORECASTIN...
INTERVAL TYPE-2 FUZZY NEURAL NETWORKS FOR SHORT-TERM ELECTRIC LOAD FORECASTIN...
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