A Non-Intrusive Heuristic for Energy Messaging Intervention Modeled Using a Novel Agent-Based Approach

Abstract

In response to the increased energy consumption in residential buildings, various efforts have been devoted to increase occupant awareness using energy feedback systems. However, it was shown that the feedback provided by these systems is not enough to inform occupant actions to reduce energy consumption. Another approach is to control energy consumption using automated energy management systems. The automatic control of appliances takes out the occupant sense of control, which is proved to be uncomfortable in many cases. This paper proposes an energy messaging intervention that keeps the control for occupants while supporting them with actionable messages. The messages inform occupants about energy waste incidents happening in their house in real time, which enables occupants to take actions to reduce their consumption. Besides, a heuristic is defined to make the intervention non-intrusive by controlling the rate and time of the messages sent to occupants. The proposed intervention is evaluated in a novel layered agentbased model. The first layer of the model generates the detailed energy consumption and realistic occupant activities. The second layer is designed to simulate the peer pressure effect on the energy consumption behavior of the individuals. The third layer is a customizable layer that simulates energy interventions. The implemented intervention in this paper is the proposed non-intrusive messaging intervention. A number of scenarios are presented in the experiments to show how the model can be used to evaluate the proposed intervention and achieve energy efficiency targets.INDEX TERMS Agent-based modeling, energy consumption, energy efficiency, energy feedback system, energy interventions, energy management system.

Influence of Occupant Behavior for Building Energy Conservation: A Systematic Review Study of Diverse Modeling and Simulation Approach

“…A limited number of earlier studies indicate the above relationship between the building occupant's behavior and energy performance [108][109][110], which is also listed in Table 1. On the one hand, an advanced knowledge of occupant behavior will help us to develop a better energy prediction model, which would contribute to better controlled algorithms and systems design [111][112][113] as well.…”

Developing a common approach for classifying building stock energy models

“…The Swan and Ugursal classification has gained wide acceptance among building stock modelers, as evidenced by its large number of citations to date in other studies. 1 The designation of "top-down" models, or those that begin with an aggregate view of a system that may subsequently be broken down into constituent sub-systems, and "bottom-up" models, or those that begin with a detailed representation of a system's constituent parts that may be aggregated up to the whole-system level, has long been used for many types of modeling. Swan and Ugursal [141] extended these concepts to the modeling of residential building stock energy use, identifying eight major types of modeling techniques under the general top-down and bottom-up categories (Fig.…”

“…Their model consists of three sub-models: people movement, thermal comfort, and energy consumption. Abdallah et al [1] evaluate the impact of a non-intrusive energy messaging intervention on energy use in the Belgian residential sector using an ABM that represents daily energy-related occupant behaviors, peer pressure effects on energy use, and the effects of messaging interventions.…”