Intelligent multi-objective control and management for smart energy efficient buildings
Abstract：Energy management in buildings has become an increasing trend in their transformation to smart and efficient in utilizing energy resources. The potential affinity of these buildings is coping energy sustain-ability, security and reliability. Building energy management has been primarily associated with development and implementation of an efficient control scheme. The challenging task of building controls is to achieve indoor building environment comfort with improved energy efficiency. In this study, multi-agent control system has been developed in combination with stochastic intelligent optimization. The multi-objective genetic algorithm (MOGA) and hybrid multi-objective genetic algorithm (HMOGA) are used as optimization algorithms. The corresponding case study simulations of effective management of energy and user comfort are presented. The developing control system provides substantial enhancement in energy efficiency and indoor environmental comfort in smart buildings. An energy efficiency of 31.6% has been achieved with an 8.1% improvement of comfort index using the HMOGA technique.
Keywords: Building； Energy management； Energy efficiency； Control system ；Optimization
- Introduction
The smart buildings concept involves the integration of technology and energy systems within buildings. This aims for energy conservation, acceptable human comfort, automation and resource management. These buildings are an upcoming trend for the future construction, enabling environment sustainability, energy security, and reliability. Buildings consume 40% of world’s primary energy, causing 30% of greenhouse gas (GHG) emission. [1]While more than 90% of the people spend most of their time inside building. [2] Building environment plays vital role for inhabitants’ productivity, morale and satisfaction. Therefore, being competitive economically, and meeting increasing environmental standards in building industry is yet an open challenge for researchers.
Building energy resource management (BERM) can help to meet the critical objectives of improving environmental quality and energy conservation in building operation. This allows inhabitants to cut their energy bills and improve quality of living comfort. Generally, three parameters determine the building’s indoor comfort-conditions: thermal, visual and air quality. [3] Thermal comfort is indicated through temperature index of inhabitants; the auxiliary heating and cooling system has been used for maintaining a comfortable indoor temperature. Visual comfort is indicated with the brilliance level; the natural as well as artificial lighting fixtures are employed for required indoor visual comfort level. Air quality indicates the CO2 concentration index; the natural and mechanical ventilation systems have been employed for an acceptable CO2 concentration level in buildings.
It is well known that thermal comfort is determined by Predictive Mean Vote (PMV) index that is generally dependent on temperature, airflow rate, humidity, mean radiant temperature and clothing. PMV however, varies between 3 and 3 on scale and fluctuates between 0.5 and 0.5, thus causing satisfaction of 90% of user. [4]Since, temperature being the most important factor in computing PMV index and is easy to measure, it has been indicated as thermal comfort factor for this study. Similarly, Visual comfort is determined through brilliance level measured in lux; other factors that include, glare, wall color reflections, etc. are sub-jective and difficult to measure. Indoor air quality has mainly been influenced by pollutants concentration in control space. However, it has been indicated that CO2 concentration can be represented with user’s presence and various other pollution sources in the building. [5] At the conference, 20 countries including the United States, China, and a number of European countries vowed to double clean energy research and development spending over 5 years. In fact, concerted efforts in the building sector which accounts for about 40% of the global primary energy consumption nowadays, have been carried out to improve sustainability of energy supply and to achieve UHI mitigation. The increased use of renewable energy sources in buildings has resulted in the development of ZEB, which are a promising approach to reduce fossil energy consumption.
The presence of an intelligent control system for building energy management (BEM) is very important. The objective of such a control system is to minimize the energy consumption and the indoor discomfort level with optimal usage of outdoor environ-mental conditions. Two factors affect these two objectives, that are, the user’s preferences and outdoor climatic conditions. In addition, BEM systems have recently been studied [6] for modern smart buildings and control systems.
Various studies for comfort control have been presented. Conventional ON/OFF and proportional integral derivative (PID) controls are widely used. [7]Prior measures were for temperature regulations inside building, that caused more energy consumption due to frequent overshoot and oscillations in comfort parameter set points. This control generally does not perform well and neither provides optimal strategy. Feedback PID controllers with constant parameters and no information of control process, usually give poor performance with large time delay in the presence of noise and nonlinearities .[8] Other advanced control schemes or artificial intelligence include predictive, [9]adaptive [10]and optimal[11] controllers. These were ensuring thermal comfort and lim
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