An Introduction to Intelligent Load Forecasting Models in Smart Power Systems

This chapter emphasizes the significance of predictive intelligent models in load prediction for smart grids, addressing a critical aspect of modern power systems. To enhance the accuracy of load demand forecasting, diverse machine learning algorithms, namely, Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), Gradient Boosting Regressor (GBR), and Random Forest (RF), were employed and rigorously evaluated using key performance indicators (KPIs) such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), Root Mean Squared Percentage Error (RMSPE), and R-squared (R2). Through a comprehensive comparison of the results derived from these models, a nuanced analysis was conducted to discern their respective strengths and weaknesses. Finally, this chapter investigates performance variations among algorithms, providing guidance for selecting and implementing suitable predictive models for smart grids, enhancing energy consumption optimization.