Feature extraction and genetic algorithm enhanced adaptive deep neural network for energy consumption prediction in buildings

Accurate forecast of energy consumption is essential in building energy management. Owing to the variation of outdoor weather condition among different seasons, year-round historical weather profile is needed to investigate its feature thoroughly. Daily weather profiles in the historical database contain various features, while different architecture of deep neural network (DNN) models may be identified suitable for specific featuring training datasets. In this study, an integrated artificial intelligence-based approach, consisting of feature extraction, evolutionary optimization and adaptive DNN model, is proposed to forecast week-ahead hourly building energy consumption. The DNN is the fundamental forecasting engine of the proposed model. Feature extraction of daily weather profile is accomplished through clustering techniques. Genetic algorithm is adopted to determine the optimal architecture of each DNN sub-model. Namely, each featuring cluster of weather profile, along with corresponding time signature and building energy consumption, is adopted to train one DNN sub-model. Therefore, the structure, activation function and training approach of DNN sub-models are adaptive to diverse featuring datasets in each cluster. To evaluate the effectiveness of the proposed predictive model, it is implemented on a real office building in the United Kingdom. Mean absolute percentage error of the training and testing cases of the proposed predictive model is 2.87% and 6.12%, which has a 24.6% and 11.9% decrease compared to DNN model with a fixed architecture. With the latest weather forecast, the devised adaptive DNN model can provide accurate week-ahead hourly energy consumption prediction for building energy management system. •An integrated AI-based approach for energy prediction, consisting of feature extraction, GA optimization and DNN model.•Genetic algorithm for optimization of DNN architecture.•Genetic algorithm for optimization of DNN architecture.•Each featuring cluster of weather profile, time signature and energy consumption, is adopted to train one DNN sub-model.•The structure, activation function and training approach of DNN model are adaptive to diverse featuring training datasets.