Short-Term Load Forecasting 2019

Edited by
February 2021
324 pages
  • ISBN978-3-03943-442-8 (Hardback)
  • ISBN978-3-03943-443-5 (PDF)

This book is a reprint of the Special Issue Short-Term Load Forecasting 2019 that was published in

Chemistry & Materials Science
Environmental & Earth Sciences
Physical Sciences
Short-term load forecasting (STLF) plays a key role in the formulation of economic, reliable, and secure operating strategies (planning, scheduling, maintenance, and control processes, among others) for a power system and will be significant in the future. However, there is still much to do in these research areas. The deployment of enabling technologies (e.g., smart meters) has made high-granularity data available for many customer segments and to approach many issues, for instance, to make forecasting tasks feasible at several demand aggregation levels. The first challenge is the improvement of STLF models and their performance at new aggregation levels. Moreover, the mix of renewables in the power system, and the necessity to include more flexibility through demand response initiatives have introduced greater uncertainties, which means new challenges for STLF in a more dynamic power system in the 2030–50 horizon. Many techniques have been proposed and applied for STLF, including traditional statistical models and AI techniques. Besides, distribution planning needs, as well as grid modernization, have initiated the development of hierarchical load forecasting. Analogously, the need to face new sources of uncertainty in the power system is giving more importance to probabilistic load forecasting. This Special Issue deals with both fundamental research and practical application research on STLF methodologies to face the challenges of a more distributed and customer-centered power system.
  • Hardback
© 2022 by the authors; CC BY-NC-ND license
short-term load forecasting; demand-side management; pattern similarity; hierarchical short-term load forecasting; feature selection; weather station selection; load forecasting; special days; regressive models; electric load forecasting; data preprocessing technique; multiobjective optimization algorithm; combined model; Nordic electricity market; electricity demand; component estimation method; univariate and multivariate time series analysis; modeling and forecasting; short-term load forecasting; deep learning; wavenet; long short-term memory; demand response; hybrid energy system; data augmentation; convolution neural network; residential load forecasting; forecasting; time series; cubic splines; real-time electricity load; seasonal patterns; Load forecasting; VSTLF; bus load forecasting; DBN; PSR; deep learning; short-term load forecasting; demand response; distributed energy resources; prosumers; short-term load forecasting; building electric energy consumption forecasting; cold-start problem; transfer learning; multivariate random forests; random forest; load forecasting; electricity consumption; lasso; Tikhonov regularization; load metering; preliminary load; short term load forecasting; performance criteria; power systems; cost analysis; short-term load forecasting; day ahead; feature extraction; deep residual neural network; multiple sources; electricity