Design, Implementation and Demonstration of Embedded Agents for Energy Management in Non-Residential Buildings

Abstract

With the building sector being responsible for 30% of the total final energy consumption, great interest lies in implementing adequate policies and deploying efficient technologies that would decrease this number. However, building comfort and energy management systems (BCEM) are challenging to manage on account of their increasing complexity with regard to the integration of renewable energy sources or the connection of electrical, thermal and gas grids. Multi-agent systems (MAS) deal well with such complex issues. This paper presents an MAS for non-residential buildings from the design, implementation and demonstration, both simulation based and in a field test. Starting from an ontology and an attached data model for BCEM application, we elaborated use cases for developing and testing the MAS framework. The building and technical equipment are modeled using the modeling language Modelica under Dymola. The agents are programmed in JADE and communicate with Dymola via TCP/IP and with the real devices via BACnet. Operatively, the agents can take on different control strategies: normal operation with no optimization, optimization of energy costs, where energy is delivered through the room through the devices that have the lowest operating costs, and relaxation of the comfort constraint, where the costs of the productivity loss under sub-optimal comfort conditions is taken into account during optimization. Comfort is expressed as a function of indoor air temperature. Simulation, including a comparison with a benchmark system, and field test results are presented to demonstrate the features of the proposed BCEM.

Scalable assessment method for agent‐based control in cyber‐physical distribution grids

“…SiL [8 –12]. Hardware‐in‐the‐Loop [13, 14]. In approach 1, the agent‐based system and the electrical grid are both simulated.…”

“…In approach 3, real (embedded) devices are used to deploy and execute agents. The agent environment is either subject to simulation [14] or represented by a real laboratory setup [13] increasing the complexity of the experiment.…”

“…Following the study presented in [6], functionality and scalability are jointly in the focus of the assessment -and not just one of the two characteristics. Often, scalability is not well addressed, since only small use cases of electrical grids with up to 100 nodes or less are studied for a proof of concept [7][8][9][10][11][12][13][14][20][21][22].…”

Upgrading BRICKS—The Context-Aware Semantic Rule-Based System for Intelligent Building Energy and Security Management

“…It takes advantage of semantic web technologies to provide a personalized, intuitive, and efficient tool interoperable with heterogeneous devices and simple to extend, maintain, and upgrade. Constantin et al [13] present an agent-based building comfort and energy management system for non-residential buildings, using semantic communications. The agents work to balance the trade-off between the occupant's comfort and the energy savings.…”

“…On one hand, some BMSs focus on buildings' energy efficiency [12,14], while others are pure building automation systems (BAS) without considering the energy management domain [15,16]. The work of Constantin et al [13] goes a step further by adding the user's comfort to the equation. On the other hand, there are only a few semantic rule-based systems in the building's domain, and not all of them are taking advantage of semantic reasoners, as seen in [17].…”