Journal ArticleDOI
Multiple regression model for fast prediction of the heating energy demand
TLDR
In this article, the authors proposed a model to predict the heating energy demand, based on the main factors that influence a building's heat consumption, such as the building global heat loss coefficient (G ), the south equivalent surface (SES), and the difference between the indoor set point temperature and the sol-air temperature.About:
This article is published in Energy and Buildings.The article was published on 2013-02-01. It has received 214 citations till now.read more
Citations
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Journal ArticleDOI
A review of data-driven building energy consumption prediction studies
Kadir Amasyali,Nora El-Gohary +1 more
TL;DR: A review of the studies that developed data-driven building energy consumption prediction models, with a particular focus on reviewing the scopes of prediction, the data properties and the data preprocessing methods used, the machine learning algorithms utilized for prediction, and the performance measures used for evaluation is provided in this paper.
Journal ArticleDOI
Regression analysis for prediction of residential energy consumption
Nelson Fumo,M. A. Rafe Biswas +1 more
TL;DR: In this article, simple and multiple linear regression analysis along with a quadratic regression analysis were performed on hourly and daily data from a research house, and the time interval of the observed data showed to be a relevant factor defining the quality of the model.
Journal ArticleDOI
A review of artificial intelligence based building energy use prediction: Contrasting the capabilities of single and ensemble prediction models
Zeyu Wang,Ravi S. Srinivasan +1 more
TL;DR: An in-depth review of single AI-based methods such as multiple linear regression, artificial neural networks, and support vector regression, and ensemble prediction method that, by combining multiple singleAI-based prediction models improves the prediction accuracy manifold.
Journal ArticleDOI
A review on the basics of building energy estimation
TL;DR: In this paper, the authors provide an up-to-date review on the basics of building energy estimation and propose a classification for energy estimation models based on the different classifications found in the literature review.
Journal ArticleDOI
Modeling heating and cooling loads by artificial intelligence for energy-efficient building design
Jui-Sheng Chou,Dac-Khuong Bui +1 more
TL;DR: In this paper, the energy performance of buildings was estimated using various data mining techniques, including support vector regression (SVR), artificial neural network (ANN), classification and regression tree, chi-squared automatic interaction detector, general linear regression, and ensemble inference model.
References
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Journal ArticleDOI
Applications of artificial neural-networks for energy systems
TL;DR: In this paper, the authors present various applications of neural networks in energy problems in a thematic rather than a chronological or any other way, including modeling and design of a solar steam generating plant, estimation of a parabolic-trough collector's intercept factor and local concentration ratio, and performance prediction of solar water-heating systems.
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Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools
TL;DR: In this article, a statistical machine learning framework was developed to study the effect of eight input variables (relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area, etc.) on two output variables, namely heating load (HL) and cooling load (CL), of residential buildings.
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Prediction of building energy consumption by using artificial neural networks
TL;DR: It is proven that ANN gives satisfactory results with deviation of 3.43% and successful prediction rate of 94.8-98.5% when compared with the outputs of the network.
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Development and validation of regression models to predict monthly heating demand for residential buildings
TL;DR: Developing of regression models to predict the monthly heating demand for single-family residential sector in temperate climates with the aim to be used by architects or design engineers as support tools in the very first stage of their projects in finding efficiently energetic solutions is concerned.
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Future trends of building heating and cooling loads and energy consumption in different climates
TL;DR: In this paper, the authors conducted multi-year building energy simulations for generic air-conditioned office buildings in Harbin, Beijing, Shanghai, Kunming and Hong Kong, representing the five major architectural climates in China.