Optimizing load demand forecasting in educational buildings using quantum-inspired particle swarm optimization (QPSO) with recurrent neural networks (RNNs):a seasonal approach.

This study uses Quantum Particle Swarm Optimization (QPSO) optimized Recurrent Neural Networks (RNN), standard RNN, and autoregressive integrated moving average (ARIMA) models to anticipate educational building power demand accurately. Energy efficiency, cost reduction, and resource allocation depend on accurate load forecasts. The study evaluates model performance using year-long load data from seasonal, daily, and hourly fluctuations. Performance indicators, including Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE), were used to assess the models. The QPSO-optimized RNN outperformed traditional RNN and ARIMA models with the lowest MAE of 15.2, MSE of 520.15, and RMSE of 22.8. Comparative investigation shows the QPSO-RNN's capacity to capture complicated load data patterns, especially during peak demand. This study shows that hybrid optimization can improve forecasting accuracy, making it a powerful tool for energy management in dynamic contexts.