Enhancing Electricity Load Forecasting with Machine Learning and Deep Learning

The electricity load forecasting handles the process of determining how much electricity will be available at a given time while maintaining the balance and stability of the power grid. The accuracy of electricity load forecasting plays an important role in ensuring safe operation and improving the...

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Published in:	Technologies (Basel) Vol. 13; no. 2; p. 59
Main Authors:	Perçuku, Arbër, Minkovska, Daniela, Hinov, Nikolay
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 01.02.2025
Subjects:	Accuracy Algorithms Alternative energy sources Artificial intelligence Case studies Datasets Deep learning Electric power systems Electrical loads Electricity electricity load Forecasting Forecasting techniques Handles Kosovo linear regression algorithm long short-term memory algorithm Machine learning Methods Neural networks Parameters Prediction theory Regression analysis short-term forecasting Substations System reliability Kosovo
ISSN:	2227-7080, 2227-7080
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Abstract	The electricity load forecasting handles the process of determining how much electricity will be available at a given time while maintaining the balance and stability of the power grid. The accuracy of electricity load forecasting plays an important role in ensuring safe operation and improving the reliability of power systems and is a key component in the operational planning and efficient market. For many years, a conventional method has been used by using historical data as input parameters. With swift progress and improvement in technology, which shows more potential due to its accuracy, different methods can be applied depending on the identified model. To enhance the forecast of load, this paper introduces and proposes a framework developed on graph database technology to archive large amounts of data, which collects measured data from electrical substations in Pristina, Kosovo. The data includes electrical and weather parameters collected over a four-year timeframe. The proposed framework is designed to handle short-term load forecasting. Machine learning Linear Regression and deep learning Long Short-Term Memory algorithms are applied to multiple datasets and mean absolute error and root mean square error are calculated. The results show the promising performance and effectiveness of the proposed model, with high accuracy in load forecasting.
AbstractList	The electricity load forecasting handles the process of determining how much electricity will be available at a given time while maintaining the balance and stability of the power grid. The accuracy of electricity load forecasting plays an important role in ensuring safe operation and improving the reliability of power systems and is a key component in the operational planning and efficient market. For many years, a conventional method has been used by using historical data as input parameters. With swift progress and improvement in technology, which shows more potential due to its accuracy, different methods can be applied depending on the identified model. To enhance the forecast of load, this paper introduces and proposes a framework developed on graph database technology to archive large amounts of data, which collects measured data from electrical substations in Pristina, Kosovo. The data includes electrical and weather parameters collected over a four-year timeframe. The proposed framework is designed to handle short-term load forecasting. Machine learning Linear Regression and deep learning Long Short-Term Memory algorithms are applied to multiple datasets and mean absolute error and root mean square error are calculated. The results show the promising performance and effectiveness of the proposed model, with high accuracy in load forecasting.
Audience	Academic
Author	Minkovska, Daniela Hinov, Nikolay Perçuku, Arbër
Author_xml	– sequence: 1 givenname: Arbër surname: Perçuku fullname: Perçuku, Arbër – sequence: 2 givenname: Daniela orcidid: 0000-0003-3095-6954 surname: Minkovska fullname: Minkovska, Daniela – sequence: 3 givenname: Nikolay orcidid: 0000-0002-0308-4858 surname: Hinov fullname: Hinov, Nikolay
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SubjectTerms	Accuracy Algorithms Alternative energy sources Artificial intelligence Case studies Datasets Deep learning Electric power systems Electrical loads Electricity electricity load Forecasting Forecasting techniques Handles Kosovo linear regression algorithm long short-term memory algorithm Machine learning Methods Neural networks Parameters Prediction theory Regression analysis short-term forecasting Substations System reliability
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Title	Enhancing Electricity Load Forecasting with Machine Learning and Deep Learning
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