Intelligent Load Forecasting for Building Energy Management Systems

As the built environment currently plays a determining role as driver of energy consumption in the developed world, with economic, social and environmental impact, better understanding of the underlying consumption models can lead to more reliable energy efficiency applications and programs. Within these frameworks, smart buildings generate large amounts of data from networks of embedded sensors and metering devices which have to be efficiently exploited for real-time decision and control. Lately, increasing attention is being given to black-box models which serve as a suitable alternative to more conventional system identification methods. In this context data-driven methods to derive reliable predictive models are needed on benchmark datasets that enable implementation and validation. We present the application and evaluation of feed-forward auto-regressive neural networks to intelligent load forecasting of smart buildings. By using public benchmarking datasets, replicable research is enabled as well as direct performance evaluation in both error metrics complexity. Experimental results are discussed with conclusion and outlook for implementation in energy management.