Using Diverse Sensors in Load Forecasting in an Office Building to Support Energy Management

The increasing penetration of renewable energy sources led to the development of several energy management approaches. One
of the main topics in this field is related to the load forecast in buildings, which can contribute to more intelligent and sustainable
energy consumption. However, it is necessary to build a proper forecast model, capable of detecting an accurate consumption
profile. The minimum effort to achieve this is to extract a historic with energy consumptions to use as input. Additional
information should be considered in order to achieve improvements in forecasting results. This way, information regarding the
day of the week is discussed as a reliable source of information that may enhance the load forecast. In this paper, two forecasting
techniques, namely neural networks and support vector machine, are used to predict the energy consumption of a building for all
5 minutes from a period. The proposed model finds the best forecasting technique and determines if the additional information
regarding the day of the week enhances the load forecast. In this case study, a period of two years and a half data with a 5-minute
time interval is used. Moreover, several tests are performed for varied inputs to understand if the insights are consistent for these
tests. This data has been adapted from an office building to illustrate the advantages of the proposed methodology.