Cloud-based building management systems using short-term cooling load forecasting

In this paper, we propose a novel cloud-based building management system (BMS) architecture for a short-term cooling load forecasting mechanism to manage the building cooling system (BCS) and reduce the cost of BCS construction and maintenance. The BCS is very important to economize on air conditioning since a huge amount of energy is consumed by the cooling system of buildings in summer time and some recent work has attempted to manage the BCS using short-term cooling load forecasting. In order to have accurate forecasts, however, excellent computing systems are necessary to predict and control the BCS based on a huge amount of past energy consumption data with rapid processing speed. Hence, in the proposed architecture, we use centralized computing resources and storages to predict and control the BCS. Furthermore, we propose a model with short-term cooling load forecasting and semantic analysis system that uses data mining techniques to improve the forecasting accuracy. Through our performance results, the proposed forecasting model outperforms another scheme in terms of the forecasting accuracy to control the BCS and it is expected that the cost of the BCS maintenance will be greatly reduced with the cloud-based BMS architecture.