Deep Learning for Energy Forecasting

This book provides an end-to-end, practice-oriented path from fundamental deep-learning concepts to state-of-the-art sequence models for time series, with a sustained focus on energy use cases. Readers learn how to formulate forecasting problems, engineer data pipelines, select and train neural architectures (RNNs, attention-based seq2seq, CNNs, and Transformers), and evaluate models with robust metrics and baselines. Dedicated chapters cover multivariate and hierarchical settings, probabilistic forecasting for uncertainty quantification, and domain-specific workflows for load and renewable generation forecasting. The final part turns models into usable systems, addressing hyperparameter optimization, reproducibility, deployment, monitoring, and practical failure modes. Primary audiences include graduate students, researchers, and practitioners who build forecasting models for electricity demand, renewable generation, and related energy time-series tasks.