Deep Learning for Energy Forecasting
From RNNs to Transformers: Building Production-Ready Forecasters
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Beschreibung
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.
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.
Bridges modern deep-learning research and real-world energy forecasting practice Provide production-ready guidance supported by practical code-oriented examples Illustrates energy-specific coverage of load and renewable forecasting
Autor*in
Xiufeng Liu
Themen in »Deep Learning for Energy Forecasting«
deep learning for time series forecasting electricity load forecasting models renewable power generation forecasting transformer models for time series sequence-to-sequence attention forecasting probabilistic forecasting and uncertainty