A Spatial Long-Term Load Forecast Using a Multiple Delineated Machine Learning Approach
Terence Kibula Lukong, Derick Nganyu Tanyu, Yannick Nkongtchou, Thomas Tamo Tatiétsé, Detlef Schulz
Energies
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 R2 score. This approach highlights the potential for enhanced, localized load forecasting in regions with data constraints.