A multi-agent intelligent decision making support system for home energy management in smart grid: A fuzzy TOPSIS approach

Abstract

In the context of intelligent home energy management in smart grid, the occupants' consumption behavior has a direct effect on the demand and supply of the electrical energy market. Correspondingly, the policies of the utility providers affect consumption behavior so techniques and tools are required to analyse the occupants' preferences, habits and lifestyles in order to support and facilitate their decision-making regarding the curtailing of their energy consumption and costs. The uncertainty about householders' preferences increases the uncertainty of appliance prioritization and makes it difficult to determine the consistency of preferences in terms of energy consumption. In this complex system, the preferences and judgments of householders are represented by linguistic and vague patterns. This paper proposes a much better representation of this linguistics that can be developed and refined by using the evaluation methods of fuzzy set theory. The proposed approach will apply the fuzzy Technique for Order Preference by Similarity to Ideal Solution (fuzzy TOPSIS) for achieving preferences. Based on our detailed literature review of the multi-agent system approach in this field, it is expected that the proposal model will offer a robust tool for communication and decision-making between occupant agents and dynamic environmental variables. It is shown that the proposed fuzzy TOPSIS approach will enable and assist householders to maximize their participation in demand response programs.

Home energy management systems: A review of modelling and complexity

“…is the predominant factor, a HEMS may also include device startup costs [21], rewards and penalties for following a desired profile [20,22], battery deterioration [32], and the cost of carbon tax [43]. Cost is the easiest objective to measure, as monetary values for these different components are readily available.…”

“…Consumption and well-being [93] Cost, consumption, emissions [16] Cost, well-being, emissions, consumption [43] requirements, as well as act based on signals received from the HEMS. Devices with these improved capabilities are called smart appliances or smart devices [21,77,106,117].…”

“…Active demand-side management system [15] Central hub controller [16] Controller [17][18][19] Demand-side control unit [20] Demand-side management system [21] Energy box [22,23] Energy consumption control unit [24] Energy consumption scheduler [25,26] Energy management controller [27] Energy management system [5,[28][29][30][31][32][33] Energy scheduler [12] ESTIA [34] Expert and predictive system [35] Gateway System [7] Home automation system [36,37] Home energy controller [38] Home energy management device [39,40] Home energy management system [41][42][43][44] Home energy management unit [45] Home gateway [46] Load manager household [47][48][49] MavHome [50,51] Optimized energy management system [52] Power scheduler [53] Residential energy management system [14,[54][55]…”

Evaluation of Combined Heat and Power (CHP) Systems Using Fuzzy Shannon Entropy and Fuzzy TOPSIS

“…Sengül et al, (2015) employed a combined fuzzy TOPSIS and Shannon's entropy to rank renewable energy supply systems in Turkey [77] and Sianaki & Masoum (2013) used fuzzy TOPSIS for home energy management in a smart grid [78]. In Figure 2, the number of the published papers from 2010 to 2015 searching with the keywords "Energy" and "Fuzzy TOPSIS" and indexed by the Web of Science database (WoS) is shown.…”

“…In Figure 2, the number of the published papers from 2010 to 2015 searching with the keywords "Energy" and "Fuzzy TOPSIS" and indexed by the Web of Science database (WoS) is shown. [78]. In Figure 2, the number of the published papers from 2010 to 2015 searching with the keywords "Energy" and "Fuzzy TOPSIS" and indexed by the Web of Science database (WoS) is shown.…”