| Keywords |
Building energy management system ; Optimal algorithm ; Artificial neural networks ; Genetic algorithm ; Energy saving |
| Abstract |
This paper discusses the modeling methodologies and optimization methods for building energy system using time series auto regression artificial neural networks. The model can be integrated into energy solution tools for building energy assessment, optimization, and many other applications. The model predicts whole building energy consumptions as function of four input variables, dry bulb and wet bulb outdoor air temperatures, hour of day and type of day. To train and test the models, data from simulations are used for the analysis. Advanced computational methods are used for data analysis and preprocessing. Different neural network structures are tested along with various input and feedback delays to determine the one yielding the best results. The optimization method was also developed to automate the process of finding the best model structure that can produce the best accurate prediction against the actual data. The results show that the developed model can provide results sufficiently accurate for its use in various energy efficiency and saving estimation applications. |