Optimization of energy forecasts in Boma: study of PSO and Leap-Nemo algorithms (2023-2053) [Optimisation des prévisions énergétiques à Boma: étude des algorithmes
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Abstract
Optimizing energy demand is crucial to ensuring sustainable development in developing countries, where imbalances between production and consumption are common. In the context of the commune of Boma, Democratic Republic of Congo, this study examines the challenges associated with energy consumption forecasting in the face of recurring power outages. The main objective is to develop an energy demand forecasting model using the PSO and LEAP-Nemo algorithms to optimize energy production from 2023 to 2053. The methodology includes the collection of qualitative and quantitative data on the infrastructure of the National Electricity Company (SNEL) and consumers, as well as a statistical analysis of the results using correlation and t-tests. The results show that the PSO model tends to overestimate energy demand, with a significant average error, while the BALU scenario offers more realistic and consistent forecasts. In conclusion, this study highlights the importance of adjusting forecasting models to improve their accuracy and recommends the integration of new variables to better capture the dynamics of energy demand in Boma.
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