Bottom-Up Short-Term Load Forecasting Considering Macro-Region and Weighting by Meteorological Region

Iuri C. Figueiró, Alzenira R. Abaide, Nelson K. Neto (), Leonardo N. F. Silva and Laura L. C. Santos
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Iuri C. Figueiró: Graduate Program in Electrical Engineering, Federal University of Santa Maria, Santa Maria 97105-900, Rio Grande do Sul, Brazil
Alzenira R. Abaide: Graduate Program in Electrical Engineering, Federal University of Santa Maria, Santa Maria 97105-900, Rio Grande do Sul, Brazil
Nelson K. Neto: Academic Coordination, Federal University of Santa Maria, Cachoeira do Sul 96503-205, Rio Grande do Sul, Brazil
Leonardo N. F. Silva: Graduate Program in Electrical Engineering, Federal University of Santa Maria, Santa Maria 97105-900, Rio Grande do Sul, Brazil
Laura L. C. Santos: Academic Coordination, Federal University of Santa Maria, Cachoeira do Sul 96503-205, Rio Grande do Sul, Brazil

Energies, 2023, vol. 16, issue 19, 1-21

Abstract: Activities related to the planning and operation of power systems use premise load forecasting, which is responsible for providing a load estimative for a given horizon that assists mainly in the operation of an electrical system. Hierarchical short-term load forecasting (STLF) becomes an approach used for this purpose, where the overall forecast is performed through system partition in smaller macro-regions and, soon after, is aggregated to compose a global forecast. In this context, this paper presents a bottom-up STLF approach for macro-regions. The main innovation is the Average Consumption per Meteorological Region (CERM) index, used to weigh the importance of each station meteorological (EM) in total load demand. Another index, the Variation of Load and Temperature (IVCT), based on historical temperature and demand changes, is proposed. These indexes are incorporated into an ANN model of the multi-layer perceptron type (MLP). The results showed a higher average performance of the index CERM and variable IVCT in relation to the other combinations performed, and the best results were used to compose the prediction of the MTR. Finally, the proposed model presented a Mean Absolute Percentage Error lower than 1%, presenting superior performance compared to an aggregate model for MTR, which shows the efficiency and contribution of the proposed methodology.

Keywords: artificial neural network; hierarchical short-term load forecasting; multi-region forecasting; meteorological variables weighting (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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