The study addresses the energy challenges faced by Lomé, where electricity demand often exceeds supply, leading to frequent load-shedding and exacerbating inequalities in access among sub-zones. It focuses on optimizing the fair distribution of electricity by combining the cake-cutting algorithm with artificial intelligence (AI) models. An analysis of existing heuristic algorithms (Random-Selector Algorithm RSA, Grouper Algorithm GA, Consumption-Sorter Algorithm CSA1, and Cost-Sorter Algorithm CSA2) revealed their limitations in ensuring equity. To address these shortcomings, a hybrid approach was developed, integrating dynamic parameters and convolutional LSTM networks coupled with wavelet transforms. Tests conducted over periods ranging from 1 to 7 days demonstrated significant performance improv ements. For instance, over 7 days, the hybrid approach (α = 0.8, β = 0.5) reduced the maximum connection gap to 20 hours, compared to 105 hours for CSA1 and 91 hours for CSA2. The proposed solution offers an efficient and equitable method for electricity d istribution, reducing inequalities while supporting the sustainable management of urban energy resources.
Bokovi et al. (Sun,) studied this question.