Application of Scheduling Techniques for Load-Shifting in Smart Homes with Renewable-Energy-Sources Integration

Abstract

The general context of this proposal is represented by the energy-efficient smart home that integrates renewable energy sources such as photovoltaic panels. The objective of this article is to minimize the amount of energy consumed from the national energy grid by producer-consumers of energy from renewable sources, in their own smart homes. In order to fulfill this goal, it was necessary to estimate the amount of renewable energy produced on the day-ahead horizon and to schedule the operation of controllable consumers in a smart home. To predict the amount of energy produced, two approaches were used: the first was based on data, and used techniques specific to artificial intelligence, more specifically, multilayer perceptron and radial-basis-function neural networks, and the second was based on models. The accuracy of the short-term prediction horizon of the techniques used was evaluated with quantitative performance indicators so that the most appropriate one in relation to the goal of the article could be selected to be used in the test scenarios. The scheduling of consumer functioning was based on their classification in relation to their ability to be controlled, and on the selection from the peer-reviewed literature of an optimization algorithm which, by load shifting from a smart home, ensured the optimal fulfillment of the objective function. The selected load-shifting algorithm was then integrated into and tested on a real database. The data used were monitored for two representative days, in terms of the amount of energy from renewable energy sources produced and consumed. The load-shifting algorithm proved its effectiveness through the results obtained and which are reported in the article.