Hierarchical Short Term Load Forecasting Considering Weighting by Meteorological Region

Authors

Keywords:

Artificial Neural Network, Hierarchical Short Term Load Forecasting, Multi Region Forecasting, Meteorological variables weighting

Abstract

Activities related to the planning and operation of power systems use as premise the load forecasting, which is responsible to provide a load estimative for a given horizon that assists mainly in the electroenergetic operation of a power system. The hierarchical short-term load forecasting 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. Then, this paper presents a hierarchical short-term forecasting approach for macro-regions, with the main contribution being the proposal of an indicator that represents the Average Consumption per Meteorological Region (CERM), to be used as weighting of each Meteorological Station (EM) as their importance for the total demand of the macro-region. This indicator is used to weight the temperature variable and then, is incorporated into a Multi-layer perceptron ANN model for the load forecasting on the horizon of 7 days ahead with hourly and daily discretization. The results showed higher average performance of the variable CERM in relation to the other combination performed, and the best results were used to compose the prediction of the Multi-Region (MTR). Finally, the proposed model presented a superior performance compared to an basis aggregate model for MTR, which shows the efficiency of the proposed methodology.

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Author Biographies

Iuri Castro Figueiró, Universidade Federal de Santa Maria - Campus de Cachoeira do Sul (UFSM-CS)

Iuri Castro Figueiró is graduated in Electrical Engineering (2011) from Universidade Federal do Pampa (UNIPAMPA) and master in electrical engineering with emphasis on energy systems (2013) from Universidade Federal de Santa Maria (UFSM). He is currently a professor at the Universidade Regional Integrada do Alto Uruguai e das Missões, campus Santo (URI Santo Ângelo) and PhD in the graduate program in Electrical Engineering, Center of Excellence in Energy and Power Systems (CEESP). He has experience in electrical engineering with emphasis on power systems.

Alzenira da Rosa Abaide, Universidade Federal de Santa Maria - Campus de Cachoeira do Sul (UFSM-CS)

Alzenira da Rosa Abaide is Professor at the Department of Electromechanics and Power Systems at the Federal University of Santa Maria (UFSM). She holds a PhD in electrical engineering from UFSM. She completed her bachelor's and master's degrees at UFSM. She coordinated the electrical engineering course for 10 years. She is a PQ (Research Productivity) at CNPq (National Council for Scientific and Technological Development), Guest Editor of
Energies. Represents CIRED - International Conference on Electricity Distribution in Brazil and was Associate Editor of the IEEE Latin America Transactions magazine. She works on research and development projects with utilities (or electric power concessionaires), currently coordinating R&D projects. Provided consultancy to CGEE - Center for Management and Strategic Studies of the Ministry of Science, Technology, and Innovation (MCTI) of Brazil for
research and preparation of a technical document. Her areas of interest are electric vehicles, power generation, planning, reliability, smart grid and optimization. Advises doctorates and master’s degrees in the Graduate Program in Electrical Engineering (CAPES 7) at UFSM.System.

Nelson Knak Neto, Universidade Federal de Santa Maria - Campus de Cachoeira do Sul (UFSM-CS)

Nelson Knak Neto is a professor at the Federal University of Santa Maria. He holds bachelor's, master's, and doctorate degrees in electrical engineering from the Federal University of Santa Maria. He was a PhD Student Guest at the Institute for Systems and Computer Engineering, Technology and Science, Porto, Portugal. His research interests are planning and operation of distribution systems, reliability, distributed energy resources, electric mobility and artificial intelligence applied to power systems.

Leonardo Nogueira Fontoura da Silva, Universidade Federal de Santa Maria - UFSM

Leonardo N. Silva (S’17) was born on 1993 in São Luiz Gonzaga-Brazil. He did his bachelor's, master’s, and Ph.D. degrees in electrical engineering at the Federal University of Santa Maria, Brazil. During a research mission, his Ph.D. was developed partially at Otto-von-Guericke University Magdeburg, Germany. He has worked as a researcher at the Center of Excellence in Energy and Power Systems since 2011, mainly in Research and Development projects with Brazilian Agents, like Petrobras and utilities. During this time, he authored more than 20 publications in well-known publishers and formalized 3 product patents. The main research interests are Electric Mobility, Load Forecasting, Power System Planning, and Distributed Energy Resources. Dr. Silva was a recipient of the IEEE Student Award, related to IEEE ISGT Latin America 2017, and the 2021 Inventor Award from Petrobras in Brazil. He is a member of IEEE and the Power and Energy Systems society.

Laura Callai dos Santos, Universidade Federal de Santa Maria - Campus de Cachoeira do Sul (UFSM-CS)

Laura Lisiane Callai dos Santos is a professor at the Federal University of Santa Maria. She is graduated in Electrical Engineering (2012) from Universidade Regional do Noroeste do Estado do Rio Grande do Sul. She holds master's and doctorate degrees in electrical engineering from the Federal University of Santa Maria. His research interests are planning and operation of distribution systems, reliability and distributed energy resources

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Published

2023-09-27

How to Cite

Castro Figueiró, I., da Rosa Abaide, A., Knak Neto, N., Nogueira Fontoura da Silva, L., & Callai dos Santos, L. (2023). Hierarchical Short Term Load Forecasting Considering Weighting by Meteorological Region. IEEE Latin America Transactions, 21(11), 1191–1198. Retrieved from https://latamt.ieeer9.org/index.php/transactions/article/view/8133

Issue

Vol. 21 No. 11 (2023): Ordinary Issue

Section

Electric Energy