Institutional buildings need smart techniques to predict the energy consumption in a smart grids’ framework. Here, the importance of dynamic load forecasting as a tool to support the decision in smart grids is addressed. In addition, it is reviewed the energy consumption patterns of institutional buildings and the state-of-the-art of load forecast modeling using artificial neural networks. The discussion is supported by historical data from energy consumption in a university building. These data are used to develop a reliable model for the prediction of the electric load in a campus. A neural network model was developed, which can forecast the load with an average error of 6.5%, and this model can also be used as a decision tool to assess the convenience of supplying this load with a set of renewable energy sources. Statistical data that measure the availability of the local renewable sources can be compared with a load model in order to assess how well these energy sources match the energy needs of buildings. This novel application of load models was applied to the campus where a good correlation (Pearson coefficient of 0.803) was found between energy demand and the availability of the solar resource in the campus.
Part of the book: AI and Learning Systems