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has been cited by the following article:

• TITLE: Thermal Energy Collection Forecasting Based on Soft Computing Techniques for Solar Heat Energy Utilization System

  AUTHORS: Atsushi Yona, Tomonobu Senjyu

  KEYWORDS: Neural Network; Tree-Based Model; Thermal Energy Collection Forecasting; Solar Heat Energy Utilization System

  JOURNAL NAME: Smart Grid and Renewable Energy, Vol.3 No.3, August 31, 2012

  ABSTRACT: In recent years, introduction of alternative energy sources such as solar energy is expected. Solar heat energy utilization systems are rapidly gaining acceptance as one of the best solutions to be an alternative energy source. However, thermal energy collection is influenced by solar radiation and weather conditions. In order to control a solar heat energy utilization system as accurate as possible, it requires method of solar radiation estimation. This paper proposes the forecast technique of a thermal energy collection of solar heat energy utilization system based on solar radiation forecasting at one-day-ahead 24-hour thermal energy collection by using three different NN models. The proposed technique with application of NN is trained by weather data based on tree-based model, and tested according to forecast day. Since tree-based-model classifies a meteorological data exactly, NN will train a solar radiation with smoothly. The validity of the proposed technique is confirmed by computer simulations by use of actual meteorological data.