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210512 ||| eng |
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|a 9783039434435
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|a 9783039434428
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|a books978-3-03943-443-5
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|a Gabaldón, Antonio
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| 245 |
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|a Short-Term Load Forecasting 2019
|h Elektronische Ressource
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| 260 |
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|a Basel, Switzerland
|b MDPI - Multidisciplinary Digital Publishing Institute
|c 2021
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| 300 |
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|a 1 electronic resource (324 p.)
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| 653 |
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|a special days
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| 653 |
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|a DBN
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| 653 |
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|a random forest
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| 653 |
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|a forecasting
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| 653 |
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|a bus load forecasting
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| 653 |
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|a short-term load forecasting
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|a data augmentation
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|a feature selection
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|a deep learning
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|a electricity consumption
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| 653 |
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|a residential load forecasting
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| 653 |
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|a regressive models
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| 653 |
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|a time series
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| 653 |
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|a History of engineering and technology / bicssc
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|a wavenet
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| 653 |
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|a seasonal patterns
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| 653 |
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|a multivariate random forests
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| 653 |
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|a PSR
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|a hybrid energy system
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| 653 |
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|a prosumers
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|a feature extraction
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|a Load forecasting
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|a Nordic electricity market
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|a performance criteria
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|a cubic splines
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| 653 |
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|a weather station selection
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|a building electric energy consumption forecasting
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|a load forecasting
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| 653 |
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|a transfer learning
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|a convolution neural network
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|a combined model
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|a multiobjective optimization algorithm
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|a component estimation method
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|a load metering
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|a electricity
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|a Tikhonov regularization
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|a day ahead
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| 653 |
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|a demand response
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| 653 |
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|a lasso
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| 653 |
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|a data preprocessing technique
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|a multiple sources
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| 653 |
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|a modeling and forecasting
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|a VSTLF
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|a univariate and multivariate time series analysis
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| 653 |
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|a deep residual neural network
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| 653 |
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|a electric load forecasting
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| 653 |
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|a hierarchical short-term load forecasting
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|a long short-term memory
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| 653 |
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|a electricity demand
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| 653 |
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|a pattern similarity
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|a cost analysis
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|a preliminary load
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|a power systems
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|a real-time electricity load
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| 653 |
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|a distributed energy resources
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|a cold-start problem
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| 653 |
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|a demand-side management
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| 653 |
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|a short term load forecasting
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| 700 |
1 |
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|a Ruiz-Abellón, Dr. María Carmen
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| 700 |
1 |
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|a Fernández-Jiménez, Luis Alfredo
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| 700 |
1 |
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|a Gabaldón, Antonio
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b DOAB
|a Directory of Open Access Books
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| 500 |
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|a Creative Commons (cc), https://creativecommons.org/licenses/by/4.0/
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| 028 |
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|a 10.3390/books978-3-03943-443-5
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| 856 |
4 |
2 |
|u https://directory.doabooks.org/handle/20.500.12854/68414
|z DOAB: description of the publication
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| 856 |
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|u https://www.mdpi.com/books/pdfview/book/3430
|7 0
|x Verlag
|3 Volltext
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|a 900
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|a 333
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|a 620
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|a 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.
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