Validating wireless power transfer (WPT) systems for electric vehicles (EVs) is a challenge due to efficiency variations caused by coil misalignments and height differences arising from various vehicle designs. Traditional simulation methods, such as finite element analysis (FEM), provide high accuracy but entail significant computational costs and calculation times, limiting the number of case studies and their optimization. This paper presents a methodology that integrates Machine Learning (ML) and Genetic Algorithms (GA) to overcome these limitations. An ML model rapidly and accurately predicts key electromagnetic parameters across a wide range of positions and frequencies. These predictions feed into a GA that optimizes control variables (voltages and frequency) with the objective of maximizing power transfer efficiency, while simultaneously ensuring component integrity at each operating point. Beyond drastically reducing simulation time and experimental effort, this methodology will enable knowledge extraction and its use for formulating design rules. These rules can lay the groundwork for developing simplified, real-time adaptive control strategies, facilitating the reduction of control variables and the narrowing of search ranges.
O. García-Izquierdo, J. F. Sanz, J. Villa et al.· Machine Learning and Knowled...· 0 citations
Predicting electrical demand in distribution systems is a fundamental problem for the efficient operation of smart grids, especially under scenarios of high temporal variability. This study compares two multi-step forecasting strategies for 24-h horizons: a direct 24→24 strategy and a recursive strategy based on sequential 24→1 predictions. Four machine learning and deep learning architectures are evaluated: LSTM, N-HiTS, U-Net, and LightGBM, using real data from electrical feeders belonging to distribution systems in the equatorial region of Ecuador. The methodology includes constructing time windows, non-overlapping train/validation/test partitioning for evaluation, consistent normalization, and comparative analysis using MAE, RMSE, and MAPE metrics. The results show that the direct 24→24 strategy achieves the best overall performance, with LSTM standing out with an approximate MAPE of 4.12%. However, the recursive strategies exhibit greater stability in the face of atypical patterns observed during holidays and weekends. Furthermore, U-Net demonstrates competitive performance in both accuracy and temporal robustness, while LightGBM stands out for its computational efficiency. It is concluded that the selection of a forecasting strategy depends on the required balance between overall accuracy, temporal stability, and computational cost.
Erik Fernando Mendez-Garces, David Buldain, M. Comech· Energies· 0 citations