Design of a New Automated Fault Detector based on artificial intelligence and Big Data Techniques

Abstract

Concerns over energy and environmental issues around the world have led to the worldwide focus on energy use reduction. Studies in each area of energy have shown that residential and commercial construction sectors consume more power than other sectors such as industry, agriculture, services, and transportation. Studies of energy consumption in building sectors have reported that energy savings of 10% to 30% can be obtained by using artificial intelligence (AI), the system would be capable of detecting and analyzing anomalies in energy usage pattern assessing, diagnosing and suggesting the best solution in suitable time. This paper proposes to integrate and hybridize between AI techniques and big data algorithms which can enhance monitoring and controlling building systems, increasing comfort and decreasing efficiently the running costs. In addition, the authors suggest a tool which aims to automatically detect abnormal energy consumption by using AI and big data which are produced by the Building Management System (BMS). This happens by designing a software application that is called Fault Detection Tool (FDT) which automatically detects the abnormalities of energy consumption, optimizes the use of different resources and analyzes faults, complaints and time taken to terminate them. Experimental results show that with the proposed approach, it is possible to accurately detect anomalous patterns in building energy consumption. This tool will be a part of an artificial intelligent decision-making system.