The document describes the development of new software to analyze and predict photovoltaic module performance. Indoor and outdoor measurements were taken to characterize module performance under different conditions. Specific software was developed using Matlab to systematically treat the large outdoor measurement data files containing years of recorded variables. The software calculates key metrics like estimated and measured energy output, module efficiency, and error between predicted and actual performance.
Test different neural networks models for forecasting of wind,solar and energ...Tonmoy Ibne Arif
In this project work, a multi-step deep neural network is used to forecast power generation and load demand for a short-term time frame. The data or feature vectors that have been used to predict the target, is a sequential time series sequence. In this project, a Recurrent Neural Network has been used in combination with a convolutional neural network to have a better forecasting model for the Windpark, Solar park and Loadpark datasets. Moreover, the forecasting performance of Feedforward neural network and Long Short Term Memory also has been compared. The whole project work has divided into two parts, in the first approach the raw dataset has been divided into a train, test split and no previous step data have been used. In the second step whole raw dataset has been divided into test, train and validation split. Additionally, current and seven previous time steps data has been fed into the model.
Outdoor testing, analysis and performance predictions of PV technologies [PV ...Smithers Apex
1. Outdoor PV performance testing provides key insights but sum kWh/kWp values alone are not enough to understand results.
2. Detailed analysis of DC module performance helps explain AC array data by accounting for factors like losses, mismatch effects, and weather impacts.
3. Techniques like normalizing voltage and current values, examining maximum power over time, and comparing to models enable identification of issues like shading, degradation, and temperature effects that influence energy yields.
The document describes the development of MATLAB software to analyze and predict photovoltaic (PV) module performance. Indoor and outdoor measurements were taken on three crystalline silicon PV modules to characterize their electrical behavior under different irradiance and temperature conditions. MATLAB programs were created to process the measurement data, plot the results, and write outputs. The software was then used to analyze the modules and predict their energy production when installed in different locations using a PV web tool.
Supporting High-Penetration PV with Energy StorageSmithers Apex
The document discusses issues with high penetration of photovoltaics (PV) and the role of energy storage solutions. High PV penetration can cause problems with generation mix, instability on high-penetration feeders, and reverse power flows. Storage can help integrate more PV by smoothing output over seconds/minutes and shaping output over an hour to conform with forecasts. Locating storage near PV or consumers maximizes value. Optimizing energy storage requires minimizing costs while capturing multiple value streams like peak shaving and grid services to allow higher levels of renewable energy on the grid.
Low Cost Utility Solar Farms Using Supersized ModulesSmithers Apex
- Utility solar farm market drivers
- Minimizing solar farm capital costs
- Crystalline silicon supersized module manufacturing
- Solar farm systems cost reduction through the use of supersized modules
- Minimizing utility solar farm LCOE by on-site deployment of supersized modules
Roger Little, Chairman & CEO, SPIRE
This document discusses the field data requirements for validating PV module performance models. It outlines that field testing is needed to understand module performance under real operating conditions compared to standardized lab tests. Key requirements for field testing include carefully selecting and characterizing modules, using high-precision calibrated equipment, properly characterizing the test site, and processing data with harmonized methods that include calculating measurement uncertainties. Meeting all these requirements allows for more comparable data across studies and better validation of models. Typical uncertainties in field performance ratio measurements are around 4.5%, while uncertainties can be reduced to around 1.5% by standardizing testing practices.
The document discusses solar photovoltaic (PV) systems, including their advantages and disadvantages. It describes the I-V characteristics of solar cells and equivalent circuit. Variations in isolation and temperature affect the PV characteristics. Losses limit conversion efficiency. Maximizing open circuit voltage, short circuit current, and fill factor leads to high performance. Solar cells are classified based on material thickness, junction structure, and active material. PV modules, panels, and arrays are also discussed. Maximum power point tracking using a buck-boost converter can optimize solar PV output. Systems can be centralized, distributed, or hybrid to serve various applications including power generation, water pumping, and lighting.
⭐⭐⭐⭐⭐ Learning-based Energy Consumption PredictionVictor Asanza
✅ Published in: https://doi.org/10.1016/j.procs.2022.07.035
As more people send information to the cloud-fog infrastructure, this brings many problems to the management of computer energy consumption. Therefore, energy consumption management of servers, fog devices and cloud computing platform should be investigated to comply with the Green IT requirement. In this paper, we propose an energy consumption prediction model consisting of several components such as hardware design, data pre-processing, characteristics extraction and selection. Our main goal is to develop a non-invasive meter based on a network of sensors that includes a microcontroller, the MQTT communication protocol and the energy measurement module. This meter measures voltage, current, power, frequency, energy and power factor while a dashboard is used to present the energy measurements in real-time. In particular, we perform measurements using a workstation that has similar characteristics to the servers of a Datacenter locate at the Information Technology Center in ESPOL,
which currently provide this type of services in Ecuador. For convenience, we evaluated different linear regression models to select the best one and to predict future energy consumption based on the several measurements from the workstation during several hours which enables the consumer to optimize and to reduce the maintenance costs of the IT equipment. The supervised machine learning algorithms presented in this work allow us to predict the energy consumption by hours and by days.
⭐ The matlab code used for data processing are available in: https://github.com/vasanza/Matlab_Code/tree/EnergyConsumptionPredictionDatacenter
⭐ The dataset used for data processing are available in:https://ieee-dataport.org/open-access/data-server-energy-consumption-dataset
✅ Read more related topics:
https://vasanza.blogspot.com/
Test different neural networks models for forecasting of wind,solar and energ...Tonmoy Ibne Arif
In this project work, a multi-step deep neural network is used to forecast power generation and load demand for a short-term time frame. The data or feature vectors that have been used to predict the target, is a sequential time series sequence. In this project, a Recurrent Neural Network has been used in combination with a convolutional neural network to have a better forecasting model for the Windpark, Solar park and Loadpark datasets. Moreover, the forecasting performance of Feedforward neural network and Long Short Term Memory also has been compared. The whole project work has divided into two parts, in the first approach the raw dataset has been divided into a train, test split and no previous step data have been used. In the second step whole raw dataset has been divided into test, train and validation split. Additionally, current and seven previous time steps data has been fed into the model.
Outdoor testing, analysis and performance predictions of PV technologies [PV ...Smithers Apex
1. Outdoor PV performance testing provides key insights but sum kWh/kWp values alone are not enough to understand results.
2. Detailed analysis of DC module performance helps explain AC array data by accounting for factors like losses, mismatch effects, and weather impacts.
3. Techniques like normalizing voltage and current values, examining maximum power over time, and comparing to models enable identification of issues like shading, degradation, and temperature effects that influence energy yields.
The document describes the development of MATLAB software to analyze and predict photovoltaic (PV) module performance. Indoor and outdoor measurements were taken on three crystalline silicon PV modules to characterize their electrical behavior under different irradiance and temperature conditions. MATLAB programs were created to process the measurement data, plot the results, and write outputs. The software was then used to analyze the modules and predict their energy production when installed in different locations using a PV web tool.
Supporting High-Penetration PV with Energy StorageSmithers Apex
The document discusses issues with high penetration of photovoltaics (PV) and the role of energy storage solutions. High PV penetration can cause problems with generation mix, instability on high-penetration feeders, and reverse power flows. Storage can help integrate more PV by smoothing output over seconds/minutes and shaping output over an hour to conform with forecasts. Locating storage near PV or consumers maximizes value. Optimizing energy storage requires minimizing costs while capturing multiple value streams like peak shaving and grid services to allow higher levels of renewable energy on the grid.
Low Cost Utility Solar Farms Using Supersized ModulesSmithers Apex
- Utility solar farm market drivers
- Minimizing solar farm capital costs
- Crystalline silicon supersized module manufacturing
- Solar farm systems cost reduction through the use of supersized modules
- Minimizing utility solar farm LCOE by on-site deployment of supersized modules
Roger Little, Chairman & CEO, SPIRE
This document discusses the field data requirements for validating PV module performance models. It outlines that field testing is needed to understand module performance under real operating conditions compared to standardized lab tests. Key requirements for field testing include carefully selecting and characterizing modules, using high-precision calibrated equipment, properly characterizing the test site, and processing data with harmonized methods that include calculating measurement uncertainties. Meeting all these requirements allows for more comparable data across studies and better validation of models. Typical uncertainties in field performance ratio measurements are around 4.5%, while uncertainties can be reduced to around 1.5% by standardizing testing practices.
The document discusses solar photovoltaic (PV) systems, including their advantages and disadvantages. It describes the I-V characteristics of solar cells and equivalent circuit. Variations in isolation and temperature affect the PV characteristics. Losses limit conversion efficiency. Maximizing open circuit voltage, short circuit current, and fill factor leads to high performance. Solar cells are classified based on material thickness, junction structure, and active material. PV modules, panels, and arrays are also discussed. Maximum power point tracking using a buck-boost converter can optimize solar PV output. Systems can be centralized, distributed, or hybrid to serve various applications including power generation, water pumping, and lighting.
⭐⭐⭐⭐⭐ Learning-based Energy Consumption PredictionVictor Asanza
✅ Published in: https://doi.org/10.1016/j.procs.2022.07.035
As more people send information to the cloud-fog infrastructure, this brings many problems to the management of computer energy consumption. Therefore, energy consumption management of servers, fog devices and cloud computing platform should be investigated to comply with the Green IT requirement. In this paper, we propose an energy consumption prediction model consisting of several components such as hardware design, data pre-processing, characteristics extraction and selection. Our main goal is to develop a non-invasive meter based on a network of sensors that includes a microcontroller, the MQTT communication protocol and the energy measurement module. This meter measures voltage, current, power, frequency, energy and power factor while a dashboard is used to present the energy measurements in real-time. In particular, we perform measurements using a workstation that has similar characteristics to the servers of a Datacenter locate at the Information Technology Center in ESPOL,
which currently provide this type of services in Ecuador. For convenience, we evaluated different linear regression models to select the best one and to predict future energy consumption based on the several measurements from the workstation during several hours which enables the consumer to optimize and to reduce the maintenance costs of the IT equipment. The supervised machine learning algorithms presented in this work allow us to predict the energy consumption by hours and by days.
⭐ The matlab code used for data processing are available in: https://github.com/vasanza/Matlab_Code/tree/EnergyConsumptionPredictionDatacenter
⭐ The dataset used for data processing are available in:https://ieee-dataport.org/open-access/data-server-energy-consumption-dataset
✅ Read more related topics:
https://vasanza.blogspot.com/
This document describes a face recognition security system that uses PCA (principal component analysis) algorithm to authenticate users. The system extracts facial features from images to generate templates, which it compares to templates in a database. If a match is found, the user is validated and an alarm circuit is deactivated. Otherwise, the alarm sounds. It concludes that the system successfully identifies users from facial images and generates monthly attendance reports for employees.
Explore how our student team leveraged data science to forecast power consumption, empowering smarter energy management and sustainability initiatives. visit for more: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Extraction of qrs complexes using automated bayesian regularization neural ne...iaemedu
The document describes a method for detecting QRS complexes in ECG signals using an automated Bayesian regularization neural network. The method involves preprocessing the ECG data using a Kaiser window bandpass filter and differentiator to remove noise and baseline drift. A feedforward neural network is then trained using Bayesian regularization to learn the characteristics of QRS complexes and detect R peaks. The algorithm achieved high performance with 98.5% detection rate, 98.41% sensitivity and 98.6% positive predictivity.
Extraction of qrs complexes using automated bayesian regularization neural ne...iaemedu
This document summarizes a research paper that presents an algorithm for detecting QRS complexes in ECG signals using a Bayesian regularization neural network. The algorithm preprocesses the ECG data using a bandpass filter and differentiation to remove noise and baseline drift. It then trains a feedforward neural network using Bayesian regularization to learn the characteristics of QRS complexes and detect R peaks. When tested on a standard ECG database, the algorithm achieved high detection performance with a detection rate of 98.5%, sensitivity of 98.41% and positive predictivity of 98.6%.
Extraction of qrs complexes using automated bayesian regularization neural ne...iaemedu
This document summarizes a research paper that presents an algorithm for detecting QRS complexes in ECG signals using a Bayesian regularization neural network. The algorithm preprocesses the ECG data using a bandpass filter and differentiation to remove noise and baseline drift. It then trains a feedforward neural network using Bayesian regularization to learn the characteristics of QRS complexes and detect R peaks. When tested on a standard ECG database, the algorithm achieved high detection performance with a detection rate of 98.5%, sensitivity of 98.41% and positive predictivity of 98.6%.
Extraction of qrs complexes using automated bayesian regularization neural ne...iaemedu
The document describes a method for detecting QRS complexes in ECG signals using an automated Bayesian regularization neural network. The method involves preprocessing the ECG data using a Kaiser window bandpass filter and differentiator to remove noise and baseline drift. A feedforward neural network is then trained using Bayesian regularization to learn the characteristics of QRS complexes and detect R peaks. The algorithm achieved high performance with 98.5% detection rate, 98.41% sensitivity and 98.6% positive predictivity.
Enabling Physics and Empirical-Based Algorithms with Spark Using the Integrat...Databricks
John Deere is a leading manufacturer of agricultural, construction and forestry machinery, diesel engines, drivetrains for a variety of applications ranging from lawn care to heavy equipment. The company collects large transient engineering datasets from John Deere test vehicles in the field, and via telematic data-loggers.
RapidMiner Linear Regression Tutorial ProcessesPPT.pdfssuser2cfd2b
This document provides steps for applying a linear regression model to a polynomial dataset using RapidMiner. It loads the polynomial data, selects the first 100 examples using a filter operator, applies a linear regression model, then uses that model to make predictions on the last 100 examples. A performance operator is used to calculate the absolute error and prediction average of the labeled vs predicted values.
This document presents information about MATLAB and how to simulate a single phase converter using Simulink. MATLAB is a technical computing environment used for tasks like math, modeling, simulation and data analysis. It features toolboxes that provide functions for specialized domains. The document outlines the steps to design a single phase converter simulation in Simulink, including building the model, running the simulation, and analyzing results like output voltage and input power factor characteristics.
MATLAB is a high-level programming language and computing environment used for numerical computations, visualization, and programming. The document discusses MATLAB's capabilities including its toolboxes, plotting functions, control structures, M-files, and user-defined functions. MATLAB is useful for engineering and scientific calculations due to its matrix-based operations and built-in functions.
ENG3104 Engineering Simulations and Computations Semester 2, 2.docxYASHU40
ENG3104 Engineering Simulations and Computations Semester 2, 2015
Assessment: Assignment 3
Due: 23 October 2015
Marks: 300
Value: 30%
1 (worth 40 marks)
1.1 Introduction
To assess how useful the wind power could be as an energy source, use the file ass2data.xls to
calculate the total energy available in the wind for each year of data.
1.2 Requirements
For this assessment item, you must produce MATLAB code which:
1. Calculates the total energy for each of the years.
2. Reports to the Command Window the energy for each year.
3. Briefly discusses whether there is any trend in the results for annual energy production.
4. Has appropriate comments throughout.
You must also calculate the total energy for the first four hours of power data (i.e. over
the first five data entries) by hand to verify your code; submit this working in a pdf file.
Your MATLAB code must test (verify) whether the computed value of energy is the same as
calculated by hand.
1.3 Assessment Criteria
Your code will be assessed using the following scheme. Note that you are marked based on how
well you perform for each category, so the correct answer determined in a basic way will receive
half marks and the correct answer determined using an excellent method/code will receive full
marks.
Quality of the code 5 marks
Quality of header(s) and comments 5 marks
Quality of calculation of the energy for each year 15 marks
Quality of reporting 5 marks
Quality of discussion 5 marks
Quality of verification based on hand calculations 5 marks
1
ENG3104 Engineering Simulations and Computations Semester 2, 2015
2 (worth 65 marks)
2.1 Introduction
For the wind turbines to operate effectively, they must turn to face into the wind. This could
create large stresses in the structure if the wind changes direction quickly while the wind speed
is high. You are to assess if this is likely to happen using the data in ass2data.xls.
2.2 Requirements
For this assessment item, you must produce MATLAB code which:
1. Calculates the instantaneous rate of change of wind direction using:
(a) backward differences
(b) forward differences
(c) central differences
2. Plots the three sets of derivatives as functions of time.
3. Produces scatter plots of maximum wind gust as functions of each of the derivatives.
4. Displays a message in the Command Window with a brief discussion of the scatter plots.
Discuss which of the derivatives should be used to compare with the wind gust and why.
Discuss whether you think the wind changes direction too quickly while the wind speed
is high and why.
5. Has appropriate comments throughout.
You must also use a backward difference, forward difference and central difference by hand to
determine the rate of change of wind direction for the twelfth data entry; submit this working
in a pdf file. Your MATLAB code must test (verify) whether these values are the same as
computed by the code for the three differences.
2.3 Assessment Criteria
Your code will ...
A Deep Dive into Spark SQL's Catalyst Optimizer with Yin HuaiDatabricks
Catalyst is becoming one of the most important components of Apache Spark, as it underpins all the major new APIs in Spark 2.0 and later versions, from DataFrames and Datasets to Streaming. At its core, Catalyst is a general library for manipulating trees.
In this talk, Yin explores a modular compiler frontend for Spark based on this library that includes a query analyzer, optimizer, and an execution planner. Yin offers a deep dive into Spark SQL’s Catalyst optimizer, introducing the core concepts of Catalyst and demonstrating how developers can extend it. You’ll leave with a deeper understanding of how Spark analyzes, optimizes, and plans a user’s query.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/06/processing-raw-images-efficiently-on-the-max78000-neural-network-accelerator-a-presentation-from-analog-devices/
Gorkem Ulkar, Principal ML Engineer at Analog Devices, presents the “Processing Raw Images Efficiently on the MAX78000 Neural Network Accelerator” tutorial at the May 2023 Embedded Vision Summit.
In this talk, Ulkar presents alternative and more efficient methods of processing raw camera images using neural network accelerators. He begins by introducing Analog Devices’ convolutional neural network accelerator, MAX78000, and showing how it achieves superior performance and energy efficiency on a range of neural network inference tasks.
In visual AI applications, cameras provide raw images not in the familiar RGB format, but in a Bayer format. In order to process these images using a neural network that was trained on RGB data, the camera images must be “de-Bayerized” to turn them into RGB images. The conventional way of performing this step is via interpolation. Unfortunately, this increases energy consumption and latency of the application since it cannot be performed by neural network accelerators. Ulkar presents alternative methods of performing this task using neural network accelerators and demonstrates the effectiveness of these techniques.
This document discusses multiphysics modeling and simulation of induction machines using various software packages. It describes ANSYS, Flux2D/Flux-Portunus co-simulation, Motor-CAD co-simulation with SpeedLab, and Maxwell co-simulation with Ephysics software. It focuses on Maxwell software for finite element analysis modeling of an induction machine and presents simulation results for magnetic field, speed, torque, and losses using Maxwell. The results are then commented on.
Classification of voltage disturbance using machine learning Mohan Kashyap
This document describes a study that uses machine learning classifiers to classify different types of voltage disturbances. A Simulink model was created to simulate various electrical faults and extract relevant features from the simulated data. Support vector machines (SVM), gradient boosting, AdaBoost and random forest classifiers were then implemented and evaluated on the extracted feature data. Evaluation metrics like confusion matrices are presented showing the performance of the different classifiers. The goal is to automatically classify voltage disturbance data to help specialists more efficiently analyze power quality issues.
IRJET - Object Identification in Steel Container through Thermal Image Pi...IRJET Journal
Thermal images of a steel container containing different objects were captured using a thermal camera. The images were filtered to remove noise and then segmented into clusters based on pixel differences, as different materials have unique thermal signatures. A pixel difference matrix map was calculated and feature vectors were extracted from scatter plots of pixel values. Average feature vector values can be used as a reference standard to identify objects inside steel containers based on their thermal properties.
This document summarizes a student project using deep learning techniques for feature selection in genome-wide association studies. The student applied patching, k-means clustering, and distance matrix calculations to reduce over 490,000 SNP features for 20 case and control subjects into new feature vectors of sizes 20x1000 and 20x10,000. This significant data reduction saves memory and allows classification algorithms to be applied to the new representations of the genetic data.
The following resources come from the 2009/10 BEng in Digital Systems and Computer Engineering (course number 2ELE0065) from the University of Hertfordshire. All the mini projects are designed as level two modules of the undergraduate programmes.
The objectives of this module are to demonstrate, within an embedded development environment:
Processor – to – processor communication
Multiple processors to perform one computation task using parallel processing
This project requires the establishment of a communication protocol between two 68000-based microcomputer systems. Using ‘C’, students will write software to control all aspects of complex data transfer system, demonstrating knowledge of handshaking, transmission protocols, transmission overhead, bandwidth, memory addressing. Students will then demonstrate and analyse parallel processing of a mathematical problem using two processors. This project requires two students working as a team.
Resumes, Cover Letters, and Applying OnlineBruce Bennett
This webinar showcases resume styles and the elements that go into building your resume. Every job application requires unique skills, and this session will show you how to improve your resume to match the jobs to which you are applying. Additionally, we will discuss cover letters and learn about ideas to include. Every job application requires unique skills so learn ways to give you the best chance of success when applying for a new position. Learn how to take advantage of all the features when uploading a job application to a company’s applicant tracking system.
This document describes a face recognition security system that uses PCA (principal component analysis) algorithm to authenticate users. The system extracts facial features from images to generate templates, which it compares to templates in a database. If a match is found, the user is validated and an alarm circuit is deactivated. Otherwise, the alarm sounds. It concludes that the system successfully identifies users from facial images and generates monthly attendance reports for employees.
Explore how our student team leveraged data science to forecast power consumption, empowering smarter energy management and sustainability initiatives. visit for more: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Extraction of qrs complexes using automated bayesian regularization neural ne...iaemedu
The document describes a method for detecting QRS complexes in ECG signals using an automated Bayesian regularization neural network. The method involves preprocessing the ECG data using a Kaiser window bandpass filter and differentiator to remove noise and baseline drift. A feedforward neural network is then trained using Bayesian regularization to learn the characteristics of QRS complexes and detect R peaks. The algorithm achieved high performance with 98.5% detection rate, 98.41% sensitivity and 98.6% positive predictivity.
Extraction of qrs complexes using automated bayesian regularization neural ne...iaemedu
This document summarizes a research paper that presents an algorithm for detecting QRS complexes in ECG signals using a Bayesian regularization neural network. The algorithm preprocesses the ECG data using a bandpass filter and differentiation to remove noise and baseline drift. It then trains a feedforward neural network using Bayesian regularization to learn the characteristics of QRS complexes and detect R peaks. When tested on a standard ECG database, the algorithm achieved high detection performance with a detection rate of 98.5%, sensitivity of 98.41% and positive predictivity of 98.6%.
Extraction of qrs complexes using automated bayesian regularization neural ne...iaemedu
This document summarizes a research paper that presents an algorithm for detecting QRS complexes in ECG signals using a Bayesian regularization neural network. The algorithm preprocesses the ECG data using a bandpass filter and differentiation to remove noise and baseline drift. It then trains a feedforward neural network using Bayesian regularization to learn the characteristics of QRS complexes and detect R peaks. When tested on a standard ECG database, the algorithm achieved high detection performance with a detection rate of 98.5%, sensitivity of 98.41% and positive predictivity of 98.6%.
Extraction of qrs complexes using automated bayesian regularization neural ne...iaemedu
The document describes a method for detecting QRS complexes in ECG signals using an automated Bayesian regularization neural network. The method involves preprocessing the ECG data using a Kaiser window bandpass filter and differentiator to remove noise and baseline drift. A feedforward neural network is then trained using Bayesian regularization to learn the characteristics of QRS complexes and detect R peaks. The algorithm achieved high performance with 98.5% detection rate, 98.41% sensitivity and 98.6% positive predictivity.
Enabling Physics and Empirical-Based Algorithms with Spark Using the Integrat...Databricks
John Deere is a leading manufacturer of agricultural, construction and forestry machinery, diesel engines, drivetrains for a variety of applications ranging from lawn care to heavy equipment. The company collects large transient engineering datasets from John Deere test vehicles in the field, and via telematic data-loggers.
RapidMiner Linear Regression Tutorial ProcessesPPT.pdfssuser2cfd2b
This document provides steps for applying a linear regression model to a polynomial dataset using RapidMiner. It loads the polynomial data, selects the first 100 examples using a filter operator, applies a linear regression model, then uses that model to make predictions on the last 100 examples. A performance operator is used to calculate the absolute error and prediction average of the labeled vs predicted values.
This document presents information about MATLAB and how to simulate a single phase converter using Simulink. MATLAB is a technical computing environment used for tasks like math, modeling, simulation and data analysis. It features toolboxes that provide functions for specialized domains. The document outlines the steps to design a single phase converter simulation in Simulink, including building the model, running the simulation, and analyzing results like output voltage and input power factor characteristics.
MATLAB is a high-level programming language and computing environment used for numerical computations, visualization, and programming. The document discusses MATLAB's capabilities including its toolboxes, plotting functions, control structures, M-files, and user-defined functions. MATLAB is useful for engineering and scientific calculations due to its matrix-based operations and built-in functions.
ENG3104 Engineering Simulations and Computations Semester 2, 2.docxYASHU40
ENG3104 Engineering Simulations and Computations Semester 2, 2015
Assessment: Assignment 3
Due: 23 October 2015
Marks: 300
Value: 30%
1 (worth 40 marks)
1.1 Introduction
To assess how useful the wind power could be as an energy source, use the file ass2data.xls to
calculate the total energy available in the wind for each year of data.
1.2 Requirements
For this assessment item, you must produce MATLAB code which:
1. Calculates the total energy for each of the years.
2. Reports to the Command Window the energy for each year.
3. Briefly discusses whether there is any trend in the results for annual energy production.
4. Has appropriate comments throughout.
You must also calculate the total energy for the first four hours of power data (i.e. over
the first five data entries) by hand to verify your code; submit this working in a pdf file.
Your MATLAB code must test (verify) whether the computed value of energy is the same as
calculated by hand.
1.3 Assessment Criteria
Your code will be assessed using the following scheme. Note that you are marked based on how
well you perform for each category, so the correct answer determined in a basic way will receive
half marks and the correct answer determined using an excellent method/code will receive full
marks.
Quality of the code 5 marks
Quality of header(s) and comments 5 marks
Quality of calculation of the energy for each year 15 marks
Quality of reporting 5 marks
Quality of discussion 5 marks
Quality of verification based on hand calculations 5 marks
1
ENG3104 Engineering Simulations and Computations Semester 2, 2015
2 (worth 65 marks)
2.1 Introduction
For the wind turbines to operate effectively, they must turn to face into the wind. This could
create large stresses in the structure if the wind changes direction quickly while the wind speed
is high. You are to assess if this is likely to happen using the data in ass2data.xls.
2.2 Requirements
For this assessment item, you must produce MATLAB code which:
1. Calculates the instantaneous rate of change of wind direction using:
(a) backward differences
(b) forward differences
(c) central differences
2. Plots the three sets of derivatives as functions of time.
3. Produces scatter plots of maximum wind gust as functions of each of the derivatives.
4. Displays a message in the Command Window with a brief discussion of the scatter plots.
Discuss which of the derivatives should be used to compare with the wind gust and why.
Discuss whether you think the wind changes direction too quickly while the wind speed
is high and why.
5. Has appropriate comments throughout.
You must also use a backward difference, forward difference and central difference by hand to
determine the rate of change of wind direction for the twelfth data entry; submit this working
in a pdf file. Your MATLAB code must test (verify) whether these values are the same as
computed by the code for the three differences.
2.3 Assessment Criteria
Your code will ...
A Deep Dive into Spark SQL's Catalyst Optimizer with Yin HuaiDatabricks
Catalyst is becoming one of the most important components of Apache Spark, as it underpins all the major new APIs in Spark 2.0 and later versions, from DataFrames and Datasets to Streaming. At its core, Catalyst is a general library for manipulating trees.
In this talk, Yin explores a modular compiler frontend for Spark based on this library that includes a query analyzer, optimizer, and an execution planner. Yin offers a deep dive into Spark SQL’s Catalyst optimizer, introducing the core concepts of Catalyst and demonstrating how developers can extend it. You’ll leave with a deeper understanding of how Spark analyzes, optimizes, and plans a user’s query.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/06/processing-raw-images-efficiently-on-the-max78000-neural-network-accelerator-a-presentation-from-analog-devices/
Gorkem Ulkar, Principal ML Engineer at Analog Devices, presents the “Processing Raw Images Efficiently on the MAX78000 Neural Network Accelerator” tutorial at the May 2023 Embedded Vision Summit.
In this talk, Ulkar presents alternative and more efficient methods of processing raw camera images using neural network accelerators. He begins by introducing Analog Devices’ convolutional neural network accelerator, MAX78000, and showing how it achieves superior performance and energy efficiency on a range of neural network inference tasks.
In visual AI applications, cameras provide raw images not in the familiar RGB format, but in a Bayer format. In order to process these images using a neural network that was trained on RGB data, the camera images must be “de-Bayerized” to turn them into RGB images. The conventional way of performing this step is via interpolation. Unfortunately, this increases energy consumption and latency of the application since it cannot be performed by neural network accelerators. Ulkar presents alternative methods of performing this task using neural network accelerators and demonstrates the effectiveness of these techniques.
This document discusses multiphysics modeling and simulation of induction machines using various software packages. It describes ANSYS, Flux2D/Flux-Portunus co-simulation, Motor-CAD co-simulation with SpeedLab, and Maxwell co-simulation with Ephysics software. It focuses on Maxwell software for finite element analysis modeling of an induction machine and presents simulation results for magnetic field, speed, torque, and losses using Maxwell. The results are then commented on.
Classification of voltage disturbance using machine learning Mohan Kashyap
This document describes a study that uses machine learning classifiers to classify different types of voltage disturbances. A Simulink model was created to simulate various electrical faults and extract relevant features from the simulated data. Support vector machines (SVM), gradient boosting, AdaBoost and random forest classifiers were then implemented and evaluated on the extracted feature data. Evaluation metrics like confusion matrices are presented showing the performance of the different classifiers. The goal is to automatically classify voltage disturbance data to help specialists more efficiently analyze power quality issues.
IRJET - Object Identification in Steel Container through Thermal Image Pi...IRJET Journal
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1. DEVELOPMENT OF NEW SOFTWARE
TO ANALYSE AND PREDICT THE
MODULE PERFORMANCE
Susana Iglesias Puente
2. OUTLINE
Performed tasks
• Indoor measurements
• Outdoor measurements
• Data treatment software
Conclusions
Suggested Software improvements
3. INDOOR MEASUREMENTS
PASAN LAPSS Laboratory
The module is characterised at each point on a matrix of
Pmax (W) as a function of Tmod and irradiance
Final result: empirical equation to estimate Pmax
result:
a + b ⋅ ln Irr + c ⋅ T AMB
Pmax =
1 + d ⋅ ln Irr + e ⋅ (ln Irr ) + f ⋅ T AMB
2
5. OUTDOOR MEASUREMENTS
Tracker:
Tracker: avoid the
effects of the angle of
incidence.
incidence.
Irradiance measured by
two different kinds of
devices:
devices: Pyranometer
and ESTI sensor.
sensor.
6. OUTDOOR MEASUREMENTS
Rack: in-plane
in-
measurements.
Result:
Result: text files
storing the different
variables involved in
module performance.
performance.
8. DATA TREATMENT SOFTWARE
Main task: development of specific software to
treat the data from the outdoor measurements.
Employed software: Matlab.
9. OUTDOOR MEASUREMENTS: data treatment
MEASUREMENTS:
The text files contain data for several years,
therefore they are large.
The data treatment using a
spreadsheet is impractical.
Solution: creation of special software to treat
the outdoor data systematically.
10. DATA TREATMENT SOFTWARE
Main objectives:
objectives:
• Obtain the values of the measured and estimated
energy produced by the module
• Obtain the energy coming from the sun (irradiation)
• To be able to calculate these at different time
intervals, e.g. day, month, year, etc.
etc.
• Compare measured and estimated energies, and
other output results numerically and graphically
11. DATA TREATMENT SOFTWARE
Software developed to treat the data:
• Solar_data_treatment
• Data_writing
• Data_plotting
• Eq_fit_params
• NOCT_estimation
• Montly_sum
• Month_teller
12. DATA TREATMENT SOFTWARE: Solar_data_treatment
SOFTWARE:
Actions carried out by the program:
program:
• Import the data from the text file.
file.
• Obtain the parameters of the empirical equation.
equation.
• Estimate Tmod from Tamb and Irradiance.
Irradiance.
• Estimate Pmax values with the empirical equation.
equation.
• Integrate Pmax over the day and store the results in
3-D arrays.
arrays.
• Obtain the energy for every month and every year.year.
• Calculate the BIAS error and the module efficiency.
efficiency.
13. DATA TREATMENT SOFTWARE: Solar_data_treatment
SOFTWARE:
Actions carried out by the program:
program:
• Import the data from the text file.
file.
• Obtain the parameters of the empirical equation.
equation.
• Estimate Tmod from Tamb and irradiance.
irradiance.
• Estimate Pmax values with the empirical equation.
equation.
• Integrate Pmax over the day and store the results in
3-D arrays.
arrays.
• Obtaining the energy for every month and every year.
year.
• Calculate the BIAS error and the module efficiency.
efficiency.
14.
15. DATA TREATMENT SOFTWARE: Solar_data_treatment
SOFTWARE:
Actions carried out by the program:
program:
• Import the data from the text file. file.
• Obtain the parameters of the empirical equation. equation.
• Estimation of Tmod with the empirical equation.
equation.
• Estimation of Pmax a + b ⋅ ln Irr + c ⋅ T AMB
Used function: Eq_fit_params the empirical equation.
values with equation.
Pmax =
• Integration of Pmax over theln Irr )andf storing the results
1 + d ⋅ ln Irr + e ⋅ ( day + ⋅ T AMB
2
in 3-D arrays.
arrays.
• Obtaining the energy for every month and every year. year.
• Calculation of the BIAS error and the module efficiency.
efficiency.
16. DATA TREATMENT SOFTWARE: Solar_data_treatment
SOFTWARE:
Actions carried out by the program:
program:
• Import the data from the text file.file.
• Obtain the parameters of the empirical equation. equation.
• Estimate Tmod from Tamb and Irradiance. Irradiance.
• Estimate Pmax values with the empirical equation. equation.
NOCT − day
Used function:MODover the 20 ⋅ Irrand
Integrate PmaxNOCT_estimation store the results in
•
T = + T AMB
3-D arrays.
arrays. 800
•
Nominal Operatingfor every month and every year.
Obtain the energy Cell Temperature year.
• Calculate the BIAS error used in empirical eqn, efficiency.
- Necessary because Tmod is
and the module efficiency.
not Tamb
17. NOCT − 20
TMOD = ⋅ G + T AMB
800
DATA TREATMENT SOFTWARE: Solar_data_treatment
SOFTWARE:
Actions carried out by the program:
program:
• Import the data from the text file. file.
• Obtain the parameters of the empirical equation.
equation.
• Estimate Tmod with the empirical equation.equation.
• Estimate Pmax values with the empirical
equation.
• Integrate Pmax over the day and store the results in
- ESTI irrad (& measured Tmod)
3-DPyran irrad (& measured Tmod)
- arrays.
arrays.
- ESTI irrad (& estimated Tmod using NOCT)
• Obtain the energy for everyusing NOCT) every year.
- Pyran irrad (& estimated Tmod month and year.
• Calculate the BIAS error and the module efficiency.
efficiency.
18. DATA TREATMENT SOFTWARE: Solar_data_treatment
SOFTWARE:
Actions carried out by the program:
program:
• Import the data from the text file.
file.
• Obtain the parameters of the empirical equation.
equation.
• Estimate Tmod with the empirical equation.
equation.
• Estimate Pmax values with the empirical equation.
• Integrate Pmax and irradiance over the day and
store the results in 3-D arrays.
• Obtain the energy for every month and every year.
year.
Used function: Monthly_sum
• Calculate the BIAS error and the module efficiency.
efficiency.
19. DATA TREATMENT SOFTWARE: Solar_data_treatment
SOFTWARE:
Data storage:
One 12x31 matrix containing the energy for every day
for each year.
These matrices are stored in the same variable to form
a 3-D array (tensor) for a number of years.
20. DATA TREATMENT SOFTWARE: Solar_data_treatment
SOFTWARE:
Actions carried out by the program:
program:
• Import the data from the text file.
file.
• Obtain the parameters of the empirical equation.
equation.
• Estimate Tmod with the empirical equation.
equation.
• Estimate Pmax values with the empirical equation.
equation.
• Integrate Pmax over the day and store the results in
3-D arrays.
arrays.
• Obtain the energy for every month and every year. year.
• Calculate the BIAS error and the module efficiency.
efficiency.
21. DATA TREATMENT SOFTWARE: Solar_data_treatment
SOFTWARE:
Actions carried out by the program:
program:
• Import the data from the text file.
file.
• Obtaining the parameters of the empirical equation.
equation.
• Estimation of Tmod with the empirical equation.
equation.
• Estimation of Pmax values with the empirical equation.
equation.
• Integration of Pmax and irradiance over the day and
storing the results in 3-D arrays.
arrays.
• Obtaining the energy for every month and every year.
year.
• Calculation of BIAS error and module efficiency.
22. DATA TREATMENT SOFTWARE: Data_writing
SOFTWARE:
Actions carried out by the program:
Creating M-files to store the calculated variables.
These data can be easily imported to a
spreadsheet (e.g. Excel) for further analysis.
23. DATA TREATMENT SOFTWARE: Data_plotting
SOFTWARE:
Actions carried out by the program:
Plotting the different variables of interest to study the
module performance.
Bar graphs were chosen instead of scatter/line
graphs.
Month_teller gives the month name that is being
plotted.
24. DATA_PLOTTING:
DATA_PLOTTING: Energy for ai01 in 2003
Module surface area=0.49 m2
Year 2003 NORMALISED MONTHLY ESTIMATES 2003
9000 9000
8000 8000
7000 7000
6000 6000
Energy (W·h)
Energy (W·h)
5000 5000
4000 4000
3000 3000
Mean monthly energy
2000 2000 Measured energy
Measured energy
Empirical ESTI energy
Empirical ESTI energy
Empirical ESTI & Tmod energy
1000 Empirical ESTI & Tmod energy 1000
0 0
1 2 3 4 5 6 7 8 9 10 11 12 2 4 6 8 10 12
Months Months
Main differences: Corrected values: divided by the
Jun, Aug, Oct, Nov, Dec number of days actually measured
per month
25. DATA_PLOTTING:
DATA_PLOTTING: Energy for ai01, Jan 2003
JANUARY 2003
400
Measured energy
• Days without
350
Empirical ESTI energy
Empirical ESTI & Tmod energy measurements
300
• Measurements
250
not carried out
Energy (W·h)
200 the same
150 amount of
100 hours every
50
day.
0
5 10 15 20 25 30
Days
26. DATA_PLOTTING:
DATA_PLOTTING: Energy for ai01, May 2003
MAY 2003
400
350
300
250
Energy (W·h)
200
150
100
Measured energy
Empirical ESTI energy
50 Empirical ESTI & Tmod energy
0
5 10 15 20 25 30
Days
27. DATA_PLOTTING:
DATA_PLOTTING: Efficiency for ai01 in 2003
Year 2003 AMBIENT AND MODULE TEMPERATURE FOR AI01 IN 2003
50
Mean efficiency ESTI
14 Measured energy <> ESTI irrad
45
Estimated ESTI energy <> ESTI irrad
Estimated ESTI & Tmod energy <> ESTI irrad
12 40
35
10
Tem perature (ºC)
Efficiency (%)
30
Energy produced by the module (W ⋅ h )
8
Efficiency (% ) =
25
⋅ 100
6 (
Energy coming from the sun W ⋅ h / 20 2 ⋅ module surface area m 2
m ) ( )
15
4
10 Ambient temperature
2 Measured module temperature
5 Estimated module temperature
0 0
2 4 6 8 10 12 1 2 3 4 5 6 7 8 9 10 11
Months Months
The efficiency is lower in summer time
when the module temperature is higher
28. DATA_PLOTTING:
DATA_PLOTTING: Efficiency for ai01, Jan 2003
JANUARY 2003
Measured energy <> ESTI irrad
14 Estimated ESTI energy <> ESTI irrad
Estimated ESTI & Tmod energy <> ESTI irrad
12
10
Efficiency (%)
8
6
4
2
0
5 10 15 20 25 30
Days
29. DATA_PLOTTING:
DATA_PLOTTING: Efficiency for ai01, May 2003
MAY 2003
Measured energy <> ESTI irrad
14 Estimated ESTI energy <> ESTI irrad
Estimated ESTI & Tmod energy <> ESTI irrad
12
10
Efficiency (%)
8
6
4
2
0
5 10 15 20 25 30
Days
30. OVERVIEW ON NUMERICAL RESULTS
Energy comparison for AI01 (polycrystalline)
Annual energy (W·h) Relative error (%)
Measured value 70345 —
Estimate ESTI 70471 0.18
Estimate Pyran 70740 0.53
Estimate
70890 0.77*
ESTI & Tmod
Estimate
71162 1.16*
Pyran & Tmod
*including December 2003 NOCT estimation with bad Tamb data
31. OVERVIEW ON NUMERICAL RESULTS
Energy comparison for LE02 (monocrystalline)
Relative
Energy, 7 months (W.h)
error (%)
Measured value 29624 —
Estimate ESTI 29401 -0.75
Estimate Pyran 29219 -1.37
Estimate
29395 -0.77
ESTI & Tmod
Estimate
29213 -1.39
Pyran & Tmod
32. ENERGY PREDICTION ON PV-GIS WEB SITE
PV-
Solar irradiation map Energywe use monthlyon
Can prediction based
(of T. Huld & M. Suri) empirical model of c-Si module
averages for energy
rating?
T. Huld new calculations:
Monthly averages based on
our meteo tower data (2003 &
2004).
Assumption: in a month the
energy is the same for every
day.
Calculate expected
instantaneous values from
sun position & airmass.
33. ENERGY PREDICTION ON PV-GIS WEB SITE
PV-
Measured Estimate Relative Estimate Relative
2003
energy (Wh) PV-GIS (Wh) error (%) Pyran (Wh) error (%)
Jan 4500 4928 9.5 4648 3.3
Feb 6007 6650 10.7 6214 3.4
Mar 7831 6032 -23.0 8018 2.4
Apr 6751 6949 2.9 6854 1.5
May 8659 8644 -0.2 8593 -0.8
Jun
“PV-
“PV-GIS type” prediction is good for a long
7006 8033 14.7 6925 -1.2
Jul period of time but not for single months
8202 8344 1.7 8256 -0.8
Aug 7471 8056 7.8 7354 -1.6
Sep 5554 6983 21.2 5513 -0.7
Oct 3546 4229 19.3 3553 0.2
Nov 1328 1964 47.9 1339 0.8
Dec 3513 2835 -14.7 3594 2.3
TOTAL 70367 73398 4.3 70740 0.5
34. CONCLUSIONS (Software)
Systematic treatment of the outdoor measurement data.
data.
Nevertheless, the program is flexible as it can be easily
modified by adding new functions.
functions.
The program can function correctly with missing data.
data.
The results are obtained in far less time than employing
a spreadsheet, and different data sets of different
lengths and from different modules can be easily
analysed.
analysed.
At the same time, the results are more reliable.
reliable.
35. Suggested Software improvements
Check the number of hours during which the
measurements were done for every day.
day.
If ∆t > 6 min, the integration of Pmax is not precise.
precise.
More parameters should be plotted, e.g. irradiance,
BIAS error, mean values, etc.
etc.
There should be taken into account that the empirical
equation to estimate Pmax can change depending on
the module.
module.
36. CONCLUSIONS (Predictions)
The empirical equation from solar simulator gives good
predictions compared with long term outdoor
measurements.
measurements.
2 Crystalline (mono and poly) modules have been
analysed.
analysed.
Comparisons with estimates based on average
irradiance and temperature data (i.e. PV-GIS) are very
(i. PV-
encouraging – proves the validity of using monthly
averages for Energy Rating purposes
37. Thanks to the RE Unit for giving me the
opportunity of participating in their projects
Thanks to Thomas Huld and all my other
colleagues for their assistance
Special thanks to my supervisor
Robert Kenny