Combinação seletiva de métodos para previsão de demanda a curtíssimo prazo em tempo real
In transforming the current electricity network, in a so called smart grid, demand forecasting is relevant to processes such as demand management, demand response, distributed generation, among others. For consumers, the replacement of electromechanical meters by electronic meters, enables real-time...
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| Tipo de recurso: | tesis doctoral |
| Estado: | Versión publicada |
| Fecha de publicación: | 2015 |
| País: | Brasil |
| Institución: | Universidade Federal de Santa Maria (UFSM) |
| Repositorio: | Manancial - Repositório Digital da UFSM |
| Idioma: | portugués |
| OAI Identifier: | oai:repositorio.ufsm.br:1/3692 |
| Acceso en línea: | http://repositorio.ufsm.br/handle/1/3692 |
| Access Level: | acceso abierto |
| Palabra clave: | Previsão de demanda Curtíssimo prazo Combinação de métodos Load forecasting Real-time methods CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA |
| Sumario: | In transforming the current electricity network, in a so called smart grid, demand forecasting is relevant to processes such as demand management, demand response, distributed generation, among others. For consumers, the replacement of electromechanical meters by electronic meters, enables real-time access to measurement data, providing this data for demand forecasting. The present work focuses on consumers with different profiles, commercial, industrial and institutional, connected to the the distribution network in medium-voltage and loads ranging between a few tens of kilowatts and two megawatts. For these consumers, very short-term demand forecasting (up to 2 hours) will be an important tool for decision making in a dynamic environment, with time-variable energy prices, demand-side management and eventually own generation. With the application of demand forecasting methods to various consumers with different profiles, it is shown that the forecasting methods with better accuracy (lower average error) are variable from consumer to consumer. For one consumer individually, the method with better accuracy is also variable, depending on the hour of the day. Combination of several demand forecasting methods results in similar or better performance compared to using only a single method. A method of selective combination is proposed, in order to eliminate the risk of choosing a unique method, which results are unpredictable. The results of the application of the proposed combination method, on several consumers with different characteristics, demonstrate that selective combination improves the quality of the forecast. |
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