Intelligent Decision Support System for Including Consumers' Preferences in Residential Energy Consumption in Smart Grid

Abstract

Smart Grid is a novel initiative the aim of which is to deliver energy to the users and also to achieve consumption efficiency by means of two-way communication. The Smart Grid architecture is a combination of various hardware devices, management and reporting software tools that are combined within an ICT infrastructure. This infrastructure is needed to make the smart grid sustainable, creative and intelligent. One of the main goals of Smart Grid is to achieve Demand Response (DR) by increasing the end users' participation in decision making and increasing the awareness that will lead them to manage their energy consumption in an efficient way. Approaches proposed in the literature achieve demand response at the different levels of the Smart Grid, but no approach focuses on the users' point of view at the home level on a continuous basis and in an intelligent way to achieve demand response. In this paper, we develop such an approach by which demand response can be achieved on a continuous basis at the home level. To achieve this, the dynamic notion of price will be utilized to develop an intelligent decision-making model that will assist the users in achieving demand response.

An intelligent approach of achieving demand response by fuzzy logic based domestic load management

“…Lacking of intelligence in home energy management have made more complex to schedule of multiple devices and manual device control is inefficient and unattractive to the residents [11]. The home energy management need to be smart enough to integrate different sources of renewable energy and optimize the best use of available power to the appliances for optimal consumptions.…”

“…Particle Swarm Optimization (PSO) method is used to optimize energy efficiency and consumer´s comfort. Another proposed work in [11], achieved demand response by utilizing dynamic notion price to develop intelligent decisionmaking model at home level for increasing the efficiency of energy consumption and adapt consumers' preferences.…”

Demand Side Management Performance Evaluation for Commercial Enterprises

“…Then, fuzzy synthetic extent of each criterion was integrated by using Equations (10)- (13). Final, the main criteria weights and the sub-criteria global weights were calculated according to Equations (14)- (16).…”

Energy Management System to Prevent Blackout in Smart Grid Network - A Case Study of Kotoka International Airport

“…Advances in information technology are expanding the potential usefulness of demand response [7] by creating the opportunity to turn the power grid into a Smart Grid [8]. As the communication of prices approaches real time, loads will be able to more precisely optimize energy use, generation, and storage [9][10] according to customer preference [11][12]. A method developed in [13] for aggregating demand response data could be used to more accurately predict customer response to operational real time changes in price.…”