TY - GEN AU - Gabaldón,Antonio AU - Ruiz-Abellón,DrMaría Carmen AU - Fernández-Jiménez,Luis Alfredo AU - Gabaldón,Antonio AU - Ruiz-Abellón,DrMaría Carmen AU - Fernández-Jiménez,Luis Alfredo TI - Short-Term Load Forecasting 2019 SN - books978-3-03943-443-5 PY - 2021/// CY - Basel, Switzerland PB - MDPI - Multidisciplinary Digital Publishing Institute KW - History of engineering & technology KW - bicssc KW - short-term load forecasting KW - demand-side management KW - pattern similarity KW - hierarchical short-term load forecasting KW - feature selection KW - weather station selection KW - load forecasting KW - special days KW - regressive models KW - electric load forecasting KW - data preprocessing technique KW - multiobjective optimization algorithm KW - combined model KW - Nordic electricity market KW - electricity demand KW - component estimation method KW - univariate and multivariate time series analysis KW - modeling and forecasting KW - deep learning KW - wavenet KW - long short-term memory KW - demand response KW - hybrid energy system KW - data augmentation KW - convolution neural network KW - residential load forecasting KW - forecasting KW - time series KW - cubic splines KW - real-time electricity load KW - seasonal patterns KW - Load forecasting KW - VSTLF KW - bus load forecasting KW - DBN KW - PSR KW - distributed energy resources KW - prosumers KW - building electric energy consumption forecasting KW - cold-start problem KW - transfer learning KW - multivariate random forests KW - random forest KW - electricity consumption KW - lasso KW - Tikhonov regularization KW - load metering KW - preliminary load KW - short term load forecasting KW - performance criteria KW - power systems KW - cost analysis KW - day ahead KW - feature extraction KW - deep residual neural network KW - multiple sources KW - electricity N1 - Open Access N2 - 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 UR - https://mdpi.com/books/pdfview/book/3430 UR - https://directory.doabooks.org/handle/20.500.12854/68414 ER -