Neural Network based downscaling of Building Energy Management System data

Abstract

Building Energy Management Systems (BEMSs) are responsible for maintaining indoor environment by controlling Heating Ventilation and Air Conditioning (HVAC) and lighting systems in buildings. Buildings worldwide account for a significant portion of world energy consumption. Thus, increasing building energy efficiency through BEMSs can result in substantial financial savings. In addition, BEMSs can significantly impact the productivity of occupants by maintaining a comfortable environment. To increase efficiency and maintain comfort, modern BEMSs rely on a large array of sensors inside the building that provide detailed data about the building state. However, due to various reasons, buildings frequently lack sufficient number of sensors, resulting in a suboptimal state awareness. In such cases, a cost effective method for increasing state awareness is needed. Therefore, this paper presents a novel method for increasing state awareness through increasing spatial resolution of data by means of data downscaling. The presented method estimates the state of occupant zones using state data gathered at floor level using Artificial Neural Networks (ANN). The presented method was tested on a real-world CO 2 dataset, and compared to a time based estimation of CO 2 concentration. The downscaling method was shown to be capable of consistently producing accurate estimates while being more accurate than time based estimations.

Fog Paradigm for Local Energy Management Systems

“…Depending on the available assets, building managers must change from typical consumers to "prosumers", being able to both produce and consume energy on their premises. Nowadays building are equipped with building energy management systems (BEMSs), which control the heating, ventilation, air conditioning (HVAC) and lighting systems to reducing energy consumption while maintaining occupants comfort [40].However, due to these ever-changing and uncertain indoor and outdoor characteristics, the performance of typical BEMS often falls short of expectation, lacking the necessary data processing, evaluation, and control methodologies [2], [41].…”