New artificial neural network prediction method for electrical consumption forecasting based on building end-uses
Due to the current high energy prices it is essential to find ways to take advantage of new energy resources and enable consumers to better understand their load curve. This understanding will help to improve customer flexibility and their ability to respond to price or other signals from the electr...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2011 |
| País: | España |
| Institución: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | inglés |
| OAI Identifier: | oai:riunet.upv.es:10251/45980 |
| Acceso en línea: | https://riunet.upv.es/handle/10251/45980 |
| Access Level: | acceso abierto |
| Palabra clave: | Artificial neural networks Building end-uses Building energy consumption Forecast method Active energy Artificial Neural Network Commercial customers Customer flexibility Demand response programs Electrical consumption Electricity market End-uses Fundamental features High energy prices In-buildings Load curves Short term prediction Total power consumption Training data sets Customer satisfaction Electric load forecasting Energy resources Energy utilization Forecasting Sales Neural networks INGENIERIA ELECTRICA |
| Sumario: | Due to the current high energy prices it is essential to find ways to take advantage of new energy resources and enable consumers to better understand their load curve. This understanding will help to improve customer flexibility and their ability to respond to price or other signals from the electricity market. In this scenario, one of the most important steps is to carry out an accurate calculation of the expected consumption curve, i.e. the baseline. Subsequently, with a proper baseline, customers can participate in demand response programs and verify performed actions. This paper presents an artificial neural network (ANN) method for short-term prediction of total power consumption in buildings with several independent processes. This problem has been widely discussed in recent literature but a new point of view is proposed. The method is based on two fundamental features: total consumption forecast based on independent processes of the considered load or end-uses; and an adequate selection of the training data set in order to simplify the ANN architecture. Validation of the method has been performed with the prediction of the whole consumption expressed as 96 active energy quarter-hourly values of the Universitat Politcnica de Valncia, a commercial customer consuming 11,500 kW. © 2011 Elsevier B.V. All rights reserved. |
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