Cooling load prediction for buildings using general regression neural networks

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

doi:10.1016/j.enconman.2003.10.009

Cited by 252 publications

(84 citation statements)

References 6 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

“…ANN has been adopted to predict building cooling loads [25,26] and annual building energy consumption levels [19], given that energy consumption datasets are highly non-stationary as regards the relationship between input variables and the outputs of a complex system [26]. Recently, there have been energy demand models using ANN that take a macroscopic approach based on macro-econometric indicators [27,28].…”

Section: Energy Consumption Modeling Researchesmentioning

confidence: 99%

An Energy and Water Resource Demand Estimation Model for Multi-Family Housing Complexes in Korea

Suh

Chang

2012

Energies

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This paper proposes and develops a residential energy and resource consumption estimation model in the context of multi-family residential housing in Korea using a multi-layer perceptron (MLP) neural network. Eight indicators are introduced which affect the energy and water resource usage characteristics of Korean residential complexes. The proposed model precisely estimated the electricity, gas energy and water consumption for each examined residential complex. In terms of validation, the results showed the highest level of agreement with actually collected datasets. The model shows promising prospects in providing necessary estimations, not only for optimally scaling and sizing energy-and water-related infrastructures, but also to promote reliable energy and resource savings through greenhouse gas (GHG) reduction planning in multi-family housing complexes. The model could also be of use in framing guidelines for the better planning of national or regional energy and resource policies and for forming a foundation of decision-making with definite references regarding the facility management of each apartment complex to enhance the energy and resource use efficiency at these locations.

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“…A comparison between the forward models and the inverse models was conducted [7,8]. Neto et al [7] developed a detailed EnergyPlus simulation model and an ANN model to predict the entire building energy consumption for an office building.…”

Section: Introductionmentioning

confidence: 99%

“…In contrast to the aforementioned work [2][3][4][5][6][7][8][9][10] that mainly focused on the development processes of machine learning models, the aim of this paper is to address the lessons, insights, and issues for application of such models for virtual and existing buildings. For this purpose, three popular machine learning algorithms (ANN, SVM, and GP) were selected, applied, and compared to a virtual and a real-life building.…”

Section: Introductionmentioning

confidence: 99%

Issues of Application of Machine Learning Models for Virtual and Real-Life Buildings

2016

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Abstract:The current Building Energy Performance Simulation (BEPS) tools are based on first principles. For the correct use of BEPS tools, simulationists should have an in-depth understanding of building physics, numerical methods, control logics of building systems, etc. However, it takes significant time and effort to develop a first principles-based simulation model for existing buildings-mainly due to the laborious process of data gathering, uncertain inputs, model calibration, etc. Rather than resorting to an expert's effort, a data-driven approach (so-called "inverse" approach) has received growing attention for the simulation of existing buildings. This paper reports a cross-comparison of three popular machine learning models (Artificial Neural Network (ANN), Support Vector Machine (SVM), and Gaussian Process (GP)) for predicting a chiller's energy consumption in a virtual and a real-life building. The predictions based on the three models are sufficiently accurate compared to the virtual and real measurements. This paper addresses the following issues for the successful development of machine learning models: reproducibility, selection of inputs, training period, outlying data obtained from the building energy management system (BEMS), and validation of the models. From the result of this comparative study, it was found that SVM has a disadvantage in computation time compared to ANN and GP. GP is the most sensitive to a training period among the three models.

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Cooling load prediction for buildings using general regression neural networks

Cited by 252 publications

References 6 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

An Energy and Water Resource Demand Estimation Model for Multi-Family Housing Complexes in Korea

Issues of Application of Machine Learning Models for Virtual and Real-Life Buildings

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