An integrated control system for optimizing the energy consumption and user comfort in buildings

Abstract

In this paper a HVAC system consisting of the thermal and ventilation subsystem and the lighting subsystem is analyzed in detail. The objective of this work is the development of an integrated control system, whose aim is to optimize the energy consumption and the user comfort. The focus of the first pan of this work is an the analysis of the different subsystems. The entire control system is described in detail. The cross coupling, which links the thermal system with the lighting system, is specified. This allows a separation ofthe control algorithms according to the requirements. The core objective of this paper is the design of several intelligent thermal control algorithms. An algorithm using a Furzy controller and an adapting power profile, a controller which uses the same algorithm as mentioned before but which optimizes the Fuzzy controller parameters using Genetic Algorithms and a generalized predictive controller (GPC) are presented. Different aspects of the control strategies, the diverse specifications of the algorithms and any restrictions concerning the implementation are listed. Last but not least simulation rcsults of different contmllers are presented and discussed.

Artificial Intelligence in Buildings: A Review of the Application of Fuzzy Logic

“…The indoor comfort goals are met. • An integrated control system that incorporates thermal comfort, visual comfort and indoor air quality was studied by Pargfrieder and Jorgl (2002). The aim is to optimize energy consumption and users' comfort simultaneously.…”

A genetic rule weighting and selection process for fuzzy control of heating, ventilating and air conditioning systems

“…By means of artificial intelligence, the system is capable of assessing, diagnosing and suggesting the best operation mode. Within the framework of machine learning, some artificial intelligence techniques could be successfully applied to enhance the HVAC System capabilities (Alcala´et al, 2001;Arima et al, 1995;Calvino et al, 2004;Huang and Nelson , 1994;Jian and Wenjian, 2000;Pargfrieder and Jo¨rgl, 2002;Rahmati et al, 2003;Yang et al, 2003) or to aid the HVAC System modeling (Angelov, 2002). In this way, the use of appropriate automatic control strategies, as fuzzy logic controllers (FLCs) (Driankov et al, 1993;Mamdani, 1974;Mamdani and Assilian, 1975), for HVAC systems control could result in important energy savings when compared to manual control, specially when they explicitly try to minimize the energy consumption (Alcala´et al, 2001;Arima et al, 1995;Huang and Nelson , 1994;Pargfrieder and Jo¨rgl, 2002).…”

“…In (Calvino et al, 2004), the more global predicted mean vote (PMV) index for thermal comfort (incorporating relative humidity and mean radiant temperature) is optimized, but again it does not explicitly optimize the energy consumption, the HVAC system stability or the indoor air quality (CO 2 concentration). In (Pargfrieder and Jo¨rgl, 2002), a FLC involving 7 variables (5 inputs and 2 outputs) is optimized by means of an evolutionary algorithm to decrement the energy consumption and to maintain a temperature setpoint, which also set aside some important criteria. In our case, five criteria will be optimized and 17 variables are considered by the FLC.…”

A multi-objective evolutionary algorithm for an effective tuning of fuzzy logic controllers in heating, ventilating and air conditioning systems

“…The energy consumption as well as indoor comfort aspects of buildings are highly dependent on the design, performance and control of their HVAC systems and equipment. Therefore, the use of appropriate automatic control strategies, such as Fuzzy Logic Controllers (FLCs) [1][2][3][4], for the control of HVAC systems could result in important energy savings when compared to manual control, particularly when we explicitly try to minimize the energy consumption [5][6][7][8][9][10].…”

“…Typically, FLCs have been applied to HVAC systems where several criteria are individually considered [5][6][7][11][12][13][14][15][16], thermal regulation, energy consumption or comfort improvement (separately). However, these and other criteria must be considered jointly [8][9][10] in order to obtain the best global performance in terms of energy consumption, desired comfort level, air quality, system stability, etc.…”