A Survey on Electric Power Demand Forecasting: Future Trends in Smart Grids, Microgrids and Smart Buildings
| Autor: | Jaime Lloret, Luis Hernandez, Javier M. Aguiar, Antonio Sánchez-Esguevillas, Belen Carro, Carlos Baladrón, Joaquim Massana |
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| Rok vydání: | 2014 |
| Předmět: |
Microgrid
Computer science business.industry Computation Smart grid INGENIERIA TELEMATICA Electric demand forecasting Short-term load forecasting Smart building Field (computer science) TECNOLOGIA ELECTRONICA Work (electrical) Risk analysis (engineering) Order (exchange) ORGANIZACION DE EMPRESAS Electrical and Electronic Engineering Adaptation (computer science) business Simulation Building automation |
| Zdroj: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia instname |
| ISSN: | 1553-877X |
| DOI: | 10.1109/surv.2014.032014.00094 |
| Popis: | Recently there has been a significant proliferation in the use of forecasting techniques, mainly due to the increased availability and power of computation systems and, in particular, to the usage of personal computers. This is also true for power network systems, where energy demand forecasting has been an important field in order to allow generation planning and adaptation. Apart from the quantitative progression, there has also been a change in the type of models proposed and used. In the '70s, the usage of non-linear techniques was generally not popular among scientists and engineers. However, in the last two decades they have become very important techniques in solving complex problems which would be very difficult to tackle otherwise. With the recent emergence of smart grids, new environments have appeared capable of integrating demand, generation, and storage. These employ intelligent and adaptive elements that require more advanced techniques for accurate and precise demand and generation forecasting in order to work optimally. This review discusses the most relevant studies on electric demand prediction over the last 40 years, and presents the different models used as well as the future trends. Additionally, it analyzes the latest studies on demand forecasting in the future environments that emerge from the usage of smart grids. |
| Databáze: | OpenAIRE |
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