An ARTMAP-incorporated Multi-Agent System for Building Intelligent Heat Management

This paper presents an ARTMAP-incorporated multi-agent system (MAS) for building heat management, which aims to maintain the desired space temperature defined by the building occupants (thermal comfort management) and improve energy efficiency by intelligently controlling the energy flow and usage in the building (building energy control). Existing MAS typically uses rule-based approaches to describe the behaviours and the processes of its agents, and the rules are fixed. The incorporation of artificial neural network (ANN) techniques to the agents can provide for the required online learning and adaptation capabilities. A three-layer MAS is proposed for building heat management and ARTMAP is incorporated into the agents so as to facilitate online learning and adaptation capabilities. Simulation results demonstrate that ARTMAP incorporated MAS provides better (automated) energy control and thermal comfort management for a building environment in comparison to its existing rule-based MAS approach

Examining the uptake of low-carbon approaches within the healthcare sector: case studies from the National Health Service in England

The National Health Service (NHS) in the UK, is one of the largest organisations in Europe and indeed the world. It therefore has a significant ecological footprint. As a result there are key corporate, financial and environmental targets that the organisation is expected to meet as a means of reducing resource consumption. Using a case study approach, this manuscript examines best practice examples for the uptake of low-carbon strategies for energy conservation. These strategies included sustainable procurement, use of renewable energy technologies, supply chain management, use of building management systems, renegotiating energy contracts, undertaking energy audits, and behaviour change, to realise significant financial, as well as energy and carbon savings. A key focus was management of water resources, including the use of recycling and recovery of heat. The implications of the findings for building ecological and financial resilience within the organisation are also discussed

Municipal Energy Management: Best Practices from DVRPC's Direct Technical Assistance Program

This guide highlights best practices and lessons learned from municipal energy management projects in southeastern Pennsylvania. In 2013 and 2014, DVRPC worked with nine municipalities in southeastern Pennsylvania to provide direct technical assistance with measuring, analyzing, and developing implementation strategies for energy management in municipal buildings. The goal of energy management is to identify opportunities for improving how energy is being used at a facility and to develop analyses that support decision making on how best to prioritize and implement these improvements. These improvements can remedy various problems -- high energy and maintenance costs due to malfunctioning, poorly installed or aging equipment, poor occupant comfort due to a lack of weatherization, or poorly controlled equipment. This guide will illustrate several best practices for identifying and implementing energy management opportunities that save money and improve building comfort

Support Vector Machine in Prediction of Building Energy Demand Using Pseudo Dynamic Approach

Building's energy consumption prediction is a major concern in the recent years and many efforts have been achieved in order to improve the energy management of buildings. In particular, the prediction of energy consumption in building is essential for the energy operator to build an optimal operating strategy, which could be integrated to building's energy management system (BEMS). This paper proposes a prediction model for building energy consumption using support vector machine (SVM). Data-driven model, for instance, SVM is very sensitive to the selection of training data. Thus the relevant days data selection method based on Dynamic Time Warping is used to train SVM model. In addition, to encompass thermal inertia of building, pseudo dynamic model is applied since it takes into account information of transition of energy consumption effects and occupancy profile. Relevant days data selection and whole training data model is applied to the case studies of Ecole des Mines de Nantes, France Office building. The results showed that support vector machine based on relevant data selection method is able to predict the energy consumption of building with a high accuracy in compare to whole data training. In addition, relevant data selection method is computationally cheaper (around 8 minute training time) in contrast to whole data training (around 31 hour for weekend and 116 hour for working days) and reveals realistic control implementation for online system as well.Comment: Proceedings of ECOS 2015-The 28th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems , Jun 2015, Pau, Franc

The role of DSM + C to facilitate the integration of renewable energy and low carbon energy technologies

Recent legislation and building regulations have aiming to reduce the energy demands of buildings and include renewable based micro-generation technologies. Due to the variations in energy delivery from these technologies, optimised control over building plant and loads is essential if we are to achieve a good demand-supply match and achieve a reduction in energy demands. This paper reports on research being undertaken as part of the UK EPSRC SuperGen Future Networks programme, specifically relating to the development of algorithms for simulating dynamic demand side control strategies to identify demand-supply matching options when deploying building integrated renewable energy and low carbon technologies. The development of demand side management and control (DSM+c) is a means to improve the dynamic demand-supply match taking account of the available demand side management capacity and time of occurrence. The principle of the developed DSM+c algorithms is to maximise the available control capacity which will enable a better demand-supply match while minimising any impact on users. This paper will demonstrate the application of DSM+c to improve the energy efficiency of a building (e.g. reduced total capacity), restructure the demand pattern via load shifting and switching (e.g. on/off or proportional control) to one more favourable to building integrated renewables. The impact of different control strategies on demand profile restructuring will be demonstrated using simulation to alter the settings of the DSM+c parameters - such as priority, methods and periods - for a given demand profile. The paper will conclude by presenting the outcomes from a case study using the decision support/design tool, MERIT where the developed DSM+c algorithms have been implemented to better facilitate the match between demand and building integrated clean energy supply technologies at the individual multi-familiy building level

Simulation-assisted control in building energy management systems

Technological advances in real-time data collection, data transfer and ever-increasing computational power are bringing simulation-assisted control and on-line fault detection and diagnosis (FDD) closer to reality than was imagined when building energy management systems (BEMSs) were introduced in the 1970s. This paper describes the development and testing of a prototype simulation-assisted controller, in which a detailed simulation program is embedded in real-time control decision making. Results from an experiment in a full-scale environmental test facility demonstrate the feasibility of predictive control using a physically-based thermal simulation program

Comparing the Online Learning Capabilities of Gaussian ARTMAP and Fuzzy ARTMAP for Building Energy Management Systems

Recently, there has been a growing interest in the application of Fuzzy ARTMAP for use in building energy management systems or EMS. However, a number of papers have indicated that there are important weaknesses to the Fuzzy ARTMAP approach, such as sensitivity to noisy data and category proliferation. Gaussian ARTMAP was developed to help overcome these weaknesses, raising the question of whether Gaussian ARTMAP could be a more effective approach for building energy management systems? This paper aims to answer this question. In particular, our results show that Gaussian ARTMAP not only has the capability to address the weaknesses of Fuzzy ARTMAP but, by doing this, provides better and more efficient EMS controls with online learning capabilities

BEMS Building Energy Management System

Voce dell’Enciclopedia Wikitecnica. BEMS Building Energy Management System (2000 battute)