Energy Efficient Smart Buildings Using Coordination Among Appliances Generating Large Data

Abstract

Internet of Things based smart grids (SGs) represent a vision of future power systems which helps to provide electricity in a smart and user friendly way. Demand side management is one of the most important component of a SG which allows energy consumers to change their electricity consumption patterns to reduce the electricity consumption cost. In this paper, we propose a home energy management system which helps to achieve our desired objectives: reduced electricity consumption cost, peak to average ratio and maximize user comfort. For this purpose, we have proposed a scheduling technique which is a hybrid of already existing optimization techniques: bacteria foraging algorithm and harmony search algorithm and is named as hybrid bacterial harmony (HBH) algorithm. Being producer of electricity units to the consumers, a utility establishes an incentive based pricing tariff; we, on top of it have employed seasonal time of use tariff which allows consumers to take decisions regarding their consumption patterns. Moreover, we introduce the concept of coordination among smart appliances using dynamic programming (DP) approach. The coordination among appliances is achieved by the help of the large data generated from the appliances of multiple homes with the joint work of heuristic techniques and DP. The resultant coordination not only reduces the electricity cost but also increases the user comfort. At last, we evaluate the performance of our proposed energy management system using our proposed optimization technique HBH. To comparatively evaluate the performance of our proposed technique, we compare it with already existing techniques. Simulation results validate that the proposed technique effectively accomplish the desired objectives while considering the consumer comfort.

Keywords

• Smart grids
• coordination
• game theory
• dynamic programming
• big data