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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Vydáno v:Technologies (Basel) Ročník 13; číslo 2; s. 59
Hlavní autoři: Perçuku, Arbër, Minkovska, Daniela, Hinov, Nikolay
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.02.2025
Témata:
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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Shrnutí: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.
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ISSN:2227-7080
2227-7080
DOI:10.3390/technologies13020059