Short-Term Electricity Load Forecasting Using a New Intelligence-Based Application

Electrical load forecasting plays a crucial role in planning and operating power plants for utility factories, as well as for policymakers seeking to devise reliable and efficient energy infrastructure. Load forecasting can be categorized into three types: long-term, mid-term, and short-term. Variou...

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Bibliographic Details
Published in:Sustainability 2023-08, Vol.15 (16), p.12311
Main Author: Khan, Salahuddin
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Artificial intelligence
Deregulation
Efficiency
Electric power systems
Electric power-plants
Electricity generation
Energy consumption
Energy industry
Forecasting
Forecasting techniques
Neural networks
Power
Power plants
Spain
Sustainability
Time series
Wavelet transforms
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Summary:Electrical load forecasting plays a crucial role in planning and operating power plants for utility factories, as well as for policymakers seeking to devise reliable and efficient energy infrastructure. Load forecasting can be categorized into three types: long-term, mid-term, and short-term. Various models, including artificial intelligence and conventional and mixed models, can be used for short-term load forecasting. Electricity load forecasting is particularly important in countries with restructured electricity markets. The accuracy of short-term load forecasting is crucial for the efficient management of electric systems. Precise forecasting offers advantages for future projects and economic activities of power system operators. In this study, a novel integrated model for short-term load forecasting has been developed, which combines the wavelet transform decomposition (WTD) model, a radial basis function network, and the Thermal Exchange Optimization (TEO) algorithm. The performance of this model was evaluated in two diverse deregulated power markets: the Pennsylvania-New Jersey-Maryland electricity market and the Spanish electricity market. The obtained results are compared with various acceptable standard forecasting models.
ISSN:2071-1050
2071-1050
DOI:10.3390/su151612311