Abstract: “The deployment of advanced metering infrastructures on the small-medium scale building facilitates power suppliers and consumers to better control the utility supply and usage chain. Data from these systems are generally used to analyze the utility usage, furthermore we can enable such as smart energy management and distribution of resources of the building. For the efficient demand-response management of building energy, we analyze the results of the energy usage pattern through clustering of the usage data of the office building. Accordingly, in this paper, we propose the RLS-based power usage prediction algorithm, the Adaptive Energy Consumption Prediction (AECP). In order to obtain the validity of our prediction model, we calculate the error rate between the measured and the actual data of the power usage. Based on this, we determine the demand for energy-efficiency with energy consumption forecast.”

Keywords: blueding energy management, energy forecasting, energy consumption, energy management, AECP