The book is also an excellent reference for practitioners and researchers who need to model and analyze time series data to generate forecasts.

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.

Since electricity plays a crucial role in industrial infrastructures of countries, power companies are trying to monitor and control infrastructures to improve energy management, scheduling and develop efficiency plans.

... 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 ...

This thesis introduces a fully data driven approach for the prediction and optimization of critical electrical grid states due to poor power quality.

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.

The conference aims at providing a platform for researchers, engineers, academics and industrial professionals to present their recent research work and to explore future trends in various areas of engineering and technology

Succinct and understandable, this book is a step-by-step guide to the mathematics and construction of electrical load forecasting models.

This book shows digital economy has become one of the most sought out solutions to sustainable development and economic growth of nations.