Deep learning methods offer a lot of promise for time series forecasting, such as the automatic learning of temporal dependence and the automatic handling of temporal structures like trends and seasonality.
This work performs a comparative study on the problem of Short-Term Load Forecast, by using different classes of state-of-the-art Recurrent Neural Networks.
... short-term wind power forecasting. ISA Trans 108:58–68 69. Vidya S, Janani ESV (2021) Wind speed multistep forecasting model using ... long short-term memory neural network. Energy 214:118980 71. Emeksiz C, Tan M (2022) Multi-step wind ...
The overarching aim of this open access book is to present self-contained theory and algorithms for investigation and prediction of electric demand peaks.
This book introduces end-to-end machine learning for the trading workflow, from the idea and feature engineering to model optimization, strategy design, and backtesting.