Abstract. In order to improve the conventional method of predicting building energy consumption using artificial neural networks (ANN), we proposed a novel neural network model to forecast building energy consumption. The model optimized the neural network based on genetic algorithm and Levenberg-Marquardt algorithm Firstly genetic algorithm was used to optimize the weight and threshold of ANN, Secondly Levenberg-Marquardt algorithm (LM) was adopted to optimize the neural network training. Then the predicting model based on the new algorithm was set up in terms of the main factors effecting the energy consumption. Furthermore, a public building electric consumption data for one month is collected to train and test the model. The testing results show that the model is more accurate and efficient than the conventional method in predicting short-term energy consumption.
Abstract-Using BP neural network in past to predict the energy consumption of the building resulted in some shortcomings. Aiming at these shortages, a new algorithm which combined genetic algorithm with Levenberg-Marquardt algorithm (LM algorithm) was proposed. The proposed algorithm was used to improve the neural network and predict the energy consumption of buildings. First, genetic algorithm was used to optimize the weight and threshold of Artificial Neural Network (ANN). Levenberg-Marquardt algorithm was adopted to optimize the neural network training. Then the predicting model was set up in terms of the main effecting factors of the energy consumption. Furthermore, a public building power consumption data for one month is collected by establishing a monitoring platform to train and test the model. Eventually, the simulation result proved that the proposed model was qualified to predict short-term energy consumption accurately and efficiently.
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