This work proposes an innovative hybrid method for accurate one-day electricity consumption forecasting. By combining artificial intelligence (AI) and statistical techniques, our hybrid model optimizes electricity production forecasting. Unlike methods used by RTE, our approach relies solely on consumption data, eliminating the need for additional variables. By preprocessing historical data and employing neural networks and statistical models, our hybrid method achieves exceptional accuracy, closely approaching RTE's performance, while significantly reducing data usage and computational demands. This promising approach streamlines electricity production planning, offering an efficient and environmentally friendly solution to environmental and economic challenges.