Energy conservation in buildings through efficient A/C control using neural networks

Ben-Nakhi, Abdullatif; Mahmoud, M. A.

doi:10.1016/s0306-2619(02)00027-2

Cited by 108 publications

(40 citation statements)

References 7 publications

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“…Within this area, ANNs have been mainly applied in several aspects of HVAC control methodologies [5][6][7][8][9][10][11], and in forecasting energy consumption [12][13][14][15][16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Neural networks based predictive control for thermal comfort and energy savings in public buildings

Ferreira¹,

Ruano²,

Silva³

et al. 2012

Energy and Buildings

377

219

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a b s t r a c tThe paper addresses the problem of controlling a Heating Ventilation and Air Conditioning (HVAC) system with the purpose of achieving a desired thermal comfort level and energy savings. The formulation uses the thermal comfort, assessed using the predicted mean vote (PMV) index, as a restriction and minimises the energy spent to comply with it. This results in the maintenance of thermal comfort and on the minimisation of energy, which in most conditions are conflicting goals requiring an optimisation method to find appropriate solutions over time. A discrete model-based predictive control methodology is applied, consisting of three major components: the predictive models, implemented by radial basis function neural networks identified by means of a multi-objective genetic algorithm; the cost function that will be optimised to minimise energy consumption and maintain thermal comfort; and the optimisation method, a discrete branch and bound approach. Each component will be described, with special emphasis on a fast and accurate computation of the PMV indices. Experimental results obtained within different rooms in a building of the University of Algarve will be presented, both in summer and winter conditions, demonstrating the feasibility and performance of the approach. Energy savings resulting from the application of the method are estimated to be greater than 50%.

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Section: Introductionmentioning

confidence: 99%

Neural networks based predictive control for thermal comfort and energy savings in public buildings

Ferreira¹,

Ruano²,

Silva³

et al. 2012

Energy and Buildings

377

219

View full text Add to dashboard Cite

show abstract

“…Control logics were developed using Matlab, particularly the Neural Network toolbox for developing ANN-based control logics. (2) Variables and Schedule Logics were tested for (1) non-application of setback and (2) application of setback. Non-application of setback adopted the constant comfort range of Table 1 On the other hand, application of setback, applied for conserving energy, employed night-time and daytime setback of thermal factors.…”

Section: Methods 21 Development Of Control Logics (1) Conventional Tmentioning

confidence: 99%

A Comparative Study Between Thermostat/Hygrometer-Based Conventional and Artificial Neural Network-Based Predictive/Adaptive Thermal Controls in Residential Buildings

Moon

Han

2012

Journal of Asian Architecture and Building Engineering

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This study aimed at testing the feasibility of employing artificial neural network (ANN)-based predictive and adaptive control logics to improve thermal comfort and energy efficiency through a decrease in overshoots and undershoots of control variables. Three control logics were developed: (1) conventional temperature/humidity control logic, (2) ANN-based temperature/humidity control logic, and (3) ANN-based Predicted Mean Vote (PMV) control logic. Performance tests were conducted in a thermal chamber for nonapplication of setback and application of setback of thermal factors. Analysis revealed that the ANN-based predictive temperature/humidity control logic generally provided greater periods of thermal comfort than that of the conventional logic, as well as a reduction in overshoots and undershoots. In addition, the ANNbased PMV control logic provided significantly better PMV conditions than both temperature/humidity based control logics. In more cases, ANN-based control logic demonstrated a reduction in electricity consumption, compared to non-ANN-based control logic, especially for a system with a large time-lag effect such as a radiant water heating system.

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“…These can be divided mainly into two groups: models to predict building energy use and algorithms for a wide range of HVAC applications, such as design, operation and fault detection [11][12][13][14].…”

Section: Prediction Of the Cooling Energy Consumption For The Next 24mentioning

confidence: 99%

Internet Based Energy Services During Cooling Season

Karatasou¹,

Geros²,

Santamouris³

2008

TOBCTJ

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Existing advanced and sophisticated building systems combined with emerging IT technologies facilitate the development of a range of new Internet based energy and environmental services. Driven mostly by the deregulation of energy market, energy utilities as well as telecommunication companies build up and test exemplars of new energy services, provided to building occupants. This paper discusses the general context of the development of e-based energy services and presents a set of such services, developed and tested in Greece. The provided energy services were the remote monitoring of environmental parameters, the remote control and actuation of cooling equipment and the on line prediction of energy consumption and environmental conditions. The services focus mainly on the cooling season, where peak power demand is rising due to the operation of air-conditioning systems. In Southern European countries, the economic and energy efficient operation of cooling appliances as well as energy management and load shedding are very important.

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Energy conservation in buildings through efficient A/C control using neural networks

Cited by 108 publications

References 7 publications

Neural networks based predictive control for thermal comfort and energy savings in public buildings

Neural networks based predictive control for thermal comfort and energy savings in public buildings

A Comparative Study Between Thermostat/Hygrometer-Based Conventional and Artificial Neural Network-Based Predictive/Adaptive Thermal Controls in Residential Buildings

Internet Based Energy Services During Cooling Season

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