Short-Term Load Forecasting 2019

• Autore: Gabaldón Antonio
• Pubblicazione: Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021
• Descrizione fisica: 1 online resource (324 p.)
• Soggetto topico: History of engineering and technology
• Soggetto non controllato: building electric energy consumption forecasting; bus load forecasting; cold-start problem; combined model; component estimation method; convolution neural network; cost analysis; cubic splines; data augmentation; data preprocessing technique; day ahead; DBN; deep learning; deep residual neural network; demand response; demand-side management; distributed energy resources; electric load forecasting; electricity; electricity consumption; electricity demand; feature extraction; feature selection; forecasting; hierarchical short-term load forecasting; hybrid energy system; lasso; load forecasting; Load forecasting; load metering; long short-term memory; modeling and forecasting; multiobjective optimization algorithm; multiple sources; multivariate random forests; Nordic electricity market; pattern similarity; performance criteria; power systems; preliminary load; prosumers; PSR; random forest; real-time electricity load; regressive models; residential load forecasting; seasonal patterns; short term load forecasting; short-term load forecasting; special days; Tikhonov regularization; time series; transfer learning; univariate and multivariate time series analysis; VSTLF; wavenet; weather station selection
• Persona (resp. second.): Ruiz-AbellónDr. María Carmen; Fernández-JiménezLuis Alfredo; GabaldónAntonio
• Sommario/riassunto: 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.
• Titolo autorizzato: Short-Term Load Forecasting 2019
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910557494303321
• Lo trovi qui: Univ. Federico II