Forecasting Electrical Load and Renewable Energy Generation Using Machine Learning Methods

This chapter per the authors examines machine learning for forecasting electrical load and renewable generation under energy transition and grid digitalization. The purpose is to show why power systems need models handling nonlinear, non-stationary, and heterogeneous data. The chapter reviews LSTM, BiLSTM, GRU, CNN, TCN, hybrid architectures, ensembles, SVR, and GPR. Solar and wind forecasting depend on weather uncertainty, spatial relationships, and telemetry quality. The chapter compares facility types with forecasting methods and discusses digital twins, adaptive forecasting, and closed-loop control. Key problems: data drift, limited transferability, interpretability, computational complexity, and SCADA cybersecurity. Development requires integrating forecasting with digital twins, XAI, reinforcement learning, anomaly detection, and cybersecurity. Machine learning becomes a foundation for proactive and resilient power system management.

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