Predicting Heating Load in Energy-Efficient Buildings Through Machine Learning Techniques

Moayedi, Hossein; Bui, Dieu Tien; Dounis, Anastasios; Lyu, Zongjie; Foong, Loke Kok

doi:10.3390/app9204338

Cited by 37 publications

(12 citation statements)

References 55 publications

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“…This paper proposes an NZEB delivery control method based on controlling the parameters that impact the energy need for heating and cooling because they are the basis of calculation of right sizing HAVC systems. The energy needs for heating and cooling are mostly analyzed factors of whole building energy needs (e.g., [13,[30][31][32][33][34]) because they are recognized as the main characteristic of building envelope in protecting the building environment from the outside climatic conditions [29].…”

Section: Motivation and Methodologymentioning

confidence: 99%

Method for Building Information Modeling Supported Project Control of Nearly Zero-Energy Building Delivery

et al. 2020

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With the increasing number of nearly zero-energy buildings (NZEB) due to increase of global awareness on climate change, the new concepts of design and control must be developed because of great NZEB dependency on detailing and multidisciplinary approach. This paper proposes a three-level gateway control method for NZEB project delivery by using digital representation of the building in building information modeling (BIM) environment. These controls (C1, C2 and C3) are introduced before three main phases of any project delivery—design phase, construction phase and handover. The proposed project control procedure uses black-box building energy modeling within the BIM environment, so the paper explores the reliability of one tool for direct energy modeling within the BIM-authoring software. The paper shows two types of validation tests with satisfactory results. This leads to conclusion that analyzed tool for energy simulation within BIM environment can be used in a way that is described in a proposed project control procedure. For further research it is proposed to explore reliability of tools for energy simulation connected to other BIM-authoring software, so this project control procedure could be independent of BIM-authoring software used in the paper.

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Section: Motivation and Methodologymentioning

confidence: 99%

Method for Building Information Modeling Supported Project Control of Nearly Zero-Energy Building Delivery

et al. 2020

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“…It has already proven itself in a wide variety of real life applications [9] such as www.ijacsa.thesai.org health [10], [13], wind speed [11], heating load in buildings. Energy efficiency [12], natural language processing [14] and bioinformatics [15]. Among the multi-output regression algorithms, we opted for the multi-output support vector regression (M-SVR) algorithm proposed by Pérez-Cruz et al [19].…”

Section: State Of the Artmentioning

confidence: 99%

M-SVR Model for a Serious Game Evaluation Tool

Omari¹,

Harchi²,

Mahtar³

et al. 2021

IJACSA

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Today, due to their interactive, participatory and entertaining nature, the Serious Games set themselves apart from other learning methods used in teaching. Much progress has been made in the design techniques and methods of Serious Games, but little in their evaluation. In order to fill this gap, we had proposed in our previous work an evaluation tool capable of helping practitioners to evaluate Serious Games in different training contexts. This evaluation tool for Serious Games is designed around four dimensions, namely the pedagogical, technological, ludic and behavioral dimensions, which are measured by clearly defined criteria. During this process, it was highlighted that the human factor (evaluator) influences considerably the result of the weightings through the choice to weight the evaluation dimensions of the Serious Games. In order to reduce this influence during the evaluation process and to keep the correlation between the variables of our evaluation system, we present in this paper, an improvement of our evaluation tool by equipping it with an intelligent supervised self-learning algorithm allowing self-regulation of the weights according to the context of use of the Serious Game to be evaluated. Thanks to the experimental verification of the optimization results, the root mean square error and the coefficient of determination are 0.016 and 98.59 percent respectively, indicating that the model has high precision which guaranteed better predictive performance. A comparison was made between this intelligent model and the models presented in our previous work; the results obtained indicated the same order of the four dimensions, and this by reducing the influence of the human factor during the Multi-Output Support Vector Regression weighting process.

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“…Furthermore, when looking at each variable separately, orientation is the variable most investigated in AI research studies. Most buildings’ energy prediction studies, such as Yeom et al (2020) , Moayedi et al (2019) , Navarro-Gonzalez & Villacampa (2019) , Seyedzadeh et al (2019) and Sadeghi et al (2020) , conducted their experiments on a dataset, created by Tsanas & Xifara (2012) of 768 records and eight characteristics (relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area, and distribution) used as predictors to estimate the energy consumption of the buildings.…”

Section: Introductionmentioning

confidence: 99%

The use of statistical and machine learning tools to accurately quantify the energy performance of residential buildings

Ibrahim

Almhafdy

Al-Shargabi

et al. 2022

PeerJ Computer Science

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Prediction of building energy consumption is key to achieving energy efficiency and sustainability. Nowadays, the analysis or prediction of building energy consumption using building energy simulation tools facilitates the design and operation of energy-efficient buildings. The collection and generation of building data are essential components of machine learning models; however, there is still a lack of such data covering certain weather conditions. Such as those related to arid climate areas. This paper fills this identified gap with the creation of a new dataset for energy consumption of 3,840 records of typical residential buildings of the Saudi Arabia region of Qassim, and investigates the impact of residential buildings’ eight input variables (Building Size, Floor Height, Glazing Area, Wall Area, window to wall ratio (WWR), Win Glazing U-value, Roof U-value, and External Wall U-value) on the heating load (HL) and cooling load (CL) output variables. A number of classical and non-parametric statistical tools are used to uncover the most strongly associated input variables with each one of the output variables. Then, the machine learning Multiple linear regression (MLR) and Multilayer perceptron (MLP) methods are used to estimate HL and CL, and their results compared using the Mean Absolute Error (MAE), the Root Mean Square Error (RMSE), and coefficient of determination (R2) performance measures. The use of the IES simulation software on the new dataset concludes that MLP accurately estimates both HL and CL with low MAE, RMSE, and R2, which evidences the feasibility and accuracy of applying machine learning methods to estimate building energy consumption.

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Predicting Heating Load in Energy-Efficient Buildings Through Machine Learning Techniques

Cited by 37 publications

References 55 publications

Method for Building Information Modeling Supported Project Control of Nearly Zero-Energy Building Delivery

Method for Building Information Modeling Supported Project Control of Nearly Zero-Energy Building Delivery

M-SVR Model for a Serious Game Evaluation Tool

The use of statistical and machine learning tools to accurately quantify the energy performance of residential buildings

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