Analysis for Non-Residential Short-Term Load Forecasting Using Machine Learning and Statistical Methods with Financial Impact on the Power Market

Short-term load forecasting predetermines how power systems operate because electricity production needs to sustain demand at all times and costs. Most load forecasts for the non-residential consumers are empirically done either by a customer’s employee or supplier personnel based on experience and...

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Autores principales:	Stefan Ungureanu, Vasile Topa, Andrei Cristinel Cziker
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	load forecasting machine learning power market forecast evaluation Technology T
Acceso en línea:	https://doaj.org/article/4cfdffaf2ee441729ebd530adcbd8804
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spelling	oai:doaj.org-article:4cfdffaf2ee441729ebd530adcbd88042021-11-11T15:47:18ZAnalysis for Non-Residential Short-Term Load Forecasting Using Machine Learning and Statistical Methods with Financial Impact on the Power Market10.3390/en142169661996-1073https://doaj.org/article/4cfdffaf2ee441729ebd530adcbd88042021-10-01T00:00:00Zhttps://www.mdpi.com/1996-1073/14/21/6966https://doaj.org/toc/1996-1073Short-term load forecasting predetermines how power systems operate because electricity production needs to sustain demand at all times and costs. Most load forecasts for the non-residential consumers are empirically done either by a customer’s employee or supplier personnel based on experience and historical data, which is frequently not consistent. Our objective is to develop viable and market-oriented machine learning models for short-term forecasting for non-residential consumers. Multiple algorithms were implemented and compared to identify the best model for a cluster of industrial and commercial consumers. The article concludes that the sliding window approach for supervised learning with recurrent neural networks can learn short and long-term dependencies in time series. The best method implemented for the 24 h forecast is a Gated Recurrent Unit (GRU) applied for aggregated loads over three months of testing data resulted in 5.28% MAPE and minimized the cost with 5326.17 € compared with the second-best method LSTM. We propose a new model to evaluate the gap between evaluation metrics and the financial impact of forecast errors in the power market environment. The model simulates bidding on the power market based on the 24 h forecast and using the Romanian day-ahead market and balancing prices through the testing dataset.Stefan UngureanuVasile TopaAndrei Cristinel CzikerMDPI AGarticleload forecastingmachine learningpower marketforecast evaluationTechnologyTENEnergies, Vol 14, Iss 6966, p 6966 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	load forecasting machine learning power market forecast evaluation Technology T
spellingShingle	load forecasting machine learning power market forecast evaluation Technology T Stefan Ungureanu Vasile Topa Andrei Cristinel Cziker Analysis for Non-Residential Short-Term Load Forecasting Using Machine Learning and Statistical Methods with Financial Impact on the Power Market
description	Short-term load forecasting predetermines how power systems operate because electricity production needs to sustain demand at all times and costs. Most load forecasts for the non-residential consumers are empirically done either by a customer’s employee or supplier personnel based on experience and historical data, which is frequently not consistent. Our objective is to develop viable and market-oriented machine learning models for short-term forecasting for non-residential consumers. Multiple algorithms were implemented and compared to identify the best model for a cluster of industrial and commercial consumers. The article concludes that the sliding window approach for supervised learning with recurrent neural networks can learn short and long-term dependencies in time series. The best method implemented for the 24 h forecast is a Gated Recurrent Unit (GRU) applied for aggregated loads over three months of testing data resulted in 5.28% MAPE and minimized the cost with 5326.17 € compared with the second-best method LSTM. We propose a new model to evaluate the gap between evaluation metrics and the financial impact of forecast errors in the power market environment. The model simulates bidding on the power market based on the 24 h forecast and using the Romanian day-ahead market and balancing prices through the testing dataset.
format	article
author	Stefan Ungureanu Vasile Topa Andrei Cristinel Cziker
author_facet	Stefan Ungureanu Vasile Topa Andrei Cristinel Cziker
author_sort	Stefan Ungureanu
title	Analysis for Non-Residential Short-Term Load Forecasting Using Machine Learning and Statistical Methods with Financial Impact on the Power Market
title_short	Analysis for Non-Residential Short-Term Load Forecasting Using Machine Learning and Statistical Methods with Financial Impact on the Power Market
title_full	Analysis for Non-Residential Short-Term Load Forecasting Using Machine Learning and Statistical Methods with Financial Impact on the Power Market
title_fullStr	Analysis for Non-Residential Short-Term Load Forecasting Using Machine Learning and Statistical Methods with Financial Impact on the Power Market
title_full_unstemmed	Analysis for Non-Residential Short-Term Load Forecasting Using Machine Learning and Statistical Methods with Financial Impact on the Power Market
title_sort	analysis for non-residential short-term load forecasting using machine learning and statistical methods with financial impact on the power market
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/4cfdffaf2ee441729ebd530adcbd8804
work_keys_str_mv	AT stefanungureanu analysisfornonresidentialshorttermloadforecastingusingmachinelearningandstatisticalmethodswithfinancialimpactonthepowermarket AT vasiletopa analysisfornonresidentialshorttermloadforecastingusingmachinelearningandstatisticalmethodswithfinancialimpactonthepowermarket AT andreicristinelcziker analysisfornonresidentialshorttermloadforecastingusingmachinelearningandstatisticalmethodswithfinancialimpactonthepowermarket
_version_	1718434021671698432

Analysis for Non-Residential Short-Term Load Forecasting Using Machine Learning and Statistical Methods with Financial Impact on the Power Market

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