A Spatial Long-Term Load Forecast Using a Multiple Delineated Machine Learning Approach

Terence Kibula Lukong (), Derick Nganyu Tanyu, Yannick Nkongtchou, Thomas Tamo Tatietse and Detlef Schulz
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Terence Kibula Lukong: Doctoral School, National Advanced School of Engineering, University of Yaoundé I, Yaounde 8390, Cameroon
Derick Nganyu Tanyu: Centre for Industrial Mathematics (ZeTeM), University of Bremen, 28359 Bremen, Germany
Yannick Nkongtchou: Doctoral School, National Advanced School of Engineering, University of Yaoundé I, Yaounde 8390, Cameroon
Thomas Tamo Tatietse: Doctoral School, National Advanced School of Engineering, University of Yaoundé I, Yaounde 8390, Cameroon
Detlef Schulz: Department of Electrical Power Systems, Helmut Schmidt University, 22043 Hamburg, Germany

Energies, 2025, vol. 18, issue 10, 1-16

Abstract: Maintaining a balance between electricity generation and consumption is vital for ensuring grid stability and preventing disruptions. Spatial load forecasting (SLF) predicts geographical electricity demand, thereby aiding in power system planning. However, conventional methods like multiple linear regression and autoregressive integrated moving average struggle to capture the complex spatiotemporal patterns in historical data. Advanced methods like spatiotemporal graph transformers, graph convolutional networks, and improved scale-limited dynamic time warping better capture these dependencies, thereby enhancing prediction accuracy. Despite the advancements, challenges persist, particularly in developing economies with limited reliable data. This paper presents a novel SLF approach that divides the grid into predefined clusters based on regional characteristics and economic activity. For each cluster, a customized long short-term memory (LSTM) model captures unique spatiotemporal dependencies for more accurate predictions. The proposed method was tested across five load clusters using real-world data from Cameroon’s National Electricity Transmission Company and Energy Utilities. The results, compared against a linear regression model, demonstrated the superior performance of the LSTM approach across metrics like the mean absolute percentage error, root-mean-square error, mean absolute error, and R 2 score. This approach highlights the potential for enhanced, localized load forecasting in regions with data constraints.

Keywords: spatial load forecasting; network planning; LSTM model (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: 2025
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