Towards Electric Price and Load Forecasting Using CNN-Based Ensembler in Smart Grid

Medium-term electricity consumption and load forecasting in smart grids is an attractive topic of study, especially using innovative data analysis approaches for future energy consumption trends. Loss of electricity during generation and use is also a problem to be addressed. Both consumers and util...

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Autores principales:	Shahzad Aslam, Nasir Ayub, Umer Farooq, Muhammad Junaid Alvi, Fahad R. Albogamy, Gul Rukh, Syed Irtaza Haider, Ahmad Taher Azar, Rasool Bukhsh
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	smart grid electricity price forecasting energy management electricity load forecasting convolutional neural network corona virus herd immunity optimization Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/e73bf0d3aac84f8aaec6dfc93c4357f3
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spelling	oai:doaj.org-article:e73bf0d3aac84f8aaec6dfc93c4357f32021-11-25T19:03:15ZTowards Electric Price and Load Forecasting Using CNN-Based Ensembler in Smart Grid10.3390/su1322126532071-1050https://doaj.org/article/e73bf0d3aac84f8aaec6dfc93c4357f32021-11-01T00:00:00Zhttps://www.mdpi.com/2071-1050/13/22/12653https://doaj.org/toc/2071-1050Medium-term electricity consumption and load forecasting in smart grids is an attractive topic of study, especially using innovative data analysis approaches for future energy consumption trends. Loss of electricity during generation and use is also a problem to be addressed. Both consumers and utilities can benefit from a predictive study of electricity demand and pricing. In this study, we used a new machine learning approach called AdaBoost to identify key features from an ISO-NE dataset that includes daily consumption data over eight years. Moreover, the DT classifier and RF are widely used to extract the best features from the dataset. Moreover, we predicted the electricity load and price using machine learning techniques including support vector machine (SVM) and deep learning techniques such as a convolutional neural network (CNN). Coronavirus herd immunity optimization (CHIO), a novel optimization approach, was used to modify the hyperparameters to increase efficiency, and it used classifiers to improve the performance of our classifier. By adding additional layers to the CNN and fine-tuning its parameters, the probability of overfitting the classifier was reduced. For method validation, we compared our proposed models with several benchmarks. MAE, MAPE, MSE, RMSE, the f1 score, recall, precision, and accuracy were the measures used for performance evaluation. Moreover, seven different forms of statistical analysis were given to show why our proposed approaches are preferable. The proposed CNN-CHIO and SVM techniques had the lowest MAPE error rates of 6% and 8%, respectively, and the highest accuracy rates of 95% and 92%, respectively.Shahzad AslamNasir AyubUmer FarooqMuhammad Junaid AlviFahad R. AlbogamyGul RukhSyed Irtaza HaiderAhmad Taher AzarRasool BukhshMDPI AGarticlesmart gridelectricity price forecastingenergy managementelectricity load forecastingconvolutional neural networkcorona virus herd immunity optimizationEnvironmental effects of industries and plantsTD194-195Renewable energy sourcesTJ807-830Environmental sciencesGE1-350ENSustainability, Vol 13, Iss 12653, p 12653 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	smart grid electricity price forecasting energy management electricity load forecasting convolutional neural network corona virus herd immunity optimization Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
spellingShingle	smart grid electricity price forecasting energy management electricity load forecasting convolutional neural network corona virus herd immunity optimization Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350 Shahzad Aslam Nasir Ayub Umer Farooq Muhammad Junaid Alvi Fahad R. Albogamy Gul Rukh Syed Irtaza Haider Ahmad Taher Azar Rasool Bukhsh Towards Electric Price and Load Forecasting Using CNN-Based Ensembler in Smart Grid
description	Medium-term electricity consumption and load forecasting in smart grids is an attractive topic of study, especially using innovative data analysis approaches for future energy consumption trends. Loss of electricity during generation and use is also a problem to be addressed. Both consumers and utilities can benefit from a predictive study of electricity demand and pricing. In this study, we used a new machine learning approach called AdaBoost to identify key features from an ISO-NE dataset that includes daily consumption data over eight years. Moreover, the DT classifier and RF are widely used to extract the best features from the dataset. Moreover, we predicted the electricity load and price using machine learning techniques including support vector machine (SVM) and deep learning techniques such as a convolutional neural network (CNN). Coronavirus herd immunity optimization (CHIO), a novel optimization approach, was used to modify the hyperparameters to increase efficiency, and it used classifiers to improve the performance of our classifier. By adding additional layers to the CNN and fine-tuning its parameters, the probability of overfitting the classifier was reduced. For method validation, we compared our proposed models with several benchmarks. MAE, MAPE, MSE, RMSE, the f1 score, recall, precision, and accuracy were the measures used for performance evaluation. Moreover, seven different forms of statistical analysis were given to show why our proposed approaches are preferable. The proposed CNN-CHIO and SVM techniques had the lowest MAPE error rates of 6% and 8%, respectively, and the highest accuracy rates of 95% and 92%, respectively.
format	article
author	Shahzad Aslam Nasir Ayub Umer Farooq Muhammad Junaid Alvi Fahad R. Albogamy Gul Rukh Syed Irtaza Haider Ahmad Taher Azar Rasool Bukhsh
author_facet	Shahzad Aslam Nasir Ayub Umer Farooq Muhammad Junaid Alvi Fahad R. Albogamy Gul Rukh Syed Irtaza Haider Ahmad Taher Azar Rasool Bukhsh
author_sort	Shahzad Aslam
title	Towards Electric Price and Load Forecasting Using CNN-Based Ensembler in Smart Grid
title_short	Towards Electric Price and Load Forecasting Using CNN-Based Ensembler in Smart Grid
title_full	Towards Electric Price and Load Forecasting Using CNN-Based Ensembler in Smart Grid
title_fullStr	Towards Electric Price and Load Forecasting Using CNN-Based Ensembler in Smart Grid
title_full_unstemmed	Towards Electric Price and Load Forecasting Using CNN-Based Ensembler in Smart Grid
title_sort	towards electric price and load forecasting using cnn-based ensembler in smart grid
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/e73bf0d3aac84f8aaec6dfc93c4357f3
work_keys_str_mv	AT shahzadaslam towardselectricpriceandloadforecastingusingcnnbasedensemblerinsmartgrid AT nasirayub towardselectricpriceandloadforecastingusingcnnbasedensemblerinsmartgrid AT umerfarooq towardselectricpriceandloadforecastingusingcnnbasedensemblerinsmartgrid AT muhammadjunaidalvi towardselectricpriceandloadforecastingusingcnnbasedensemblerinsmartgrid AT fahadralbogamy towardselectricpriceandloadforecastingusingcnnbasedensemblerinsmartgrid AT gulrukh towardselectricpriceandloadforecastingusingcnnbasedensemblerinsmartgrid AT syedirtazahaider towardselectricpriceandloadforecastingusingcnnbasedensemblerinsmartgrid AT ahmadtaherazar towardselectricpriceandloadforecastingusingcnnbasedensemblerinsmartgrid AT rasoolbukhsh towardselectricpriceandloadforecastingusingcnnbasedensemblerinsmartgrid
_version_	1718410371561160704

Towards Electric Price and Load Forecasting Using CNN-Based Ensembler in Smart Grid

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