An Octahedric Regression Model of Energy Efficiency on Residential Buildings

Navarro-González, Francisco J.; Villacampa, Yolanda

doi:10.3390/app9224978

Cited by 9 publications

(8 citation statements)

References 44 publications

(60 reference statements)

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“…With a dataset containing 768 samplings of building attributes, in comparison to previous studies, they obtained a lower mean square error and better accuracy for cooling and heating energy prediction. The study by Navarro-Gonzalez and Villacampa [22] also addressed the overfitting issue using the Octahedric regression method with lower computational complexity. Because of the high demand of industrial and residential buildings for efficient smart energy techniques, Reference [29] proposed a system that utilized an ensemble deep learningbased approach to predict energy consumption via chronological dependencies.…”

Section: Related Workmentioning

confidence: 99%

Tuning Deep Neural Networks for Predicting Energy Consumption in Arid Climate Based on Buildings Characteristics

et al. 2021

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The dramatic growth in the number of buildings worldwide has led to an increase interest in predicting energy consumption, especially for the case of residential buildings. As the heating and cooling system highly affect the operation cost of buildings; it is worth investigating the development of models to predict the heating and cooling loads of buildings. In contrast to the majority of the existing related studies, which are based on historical energy consumption data, this study considers building characteristics, such as area and floor height, to develop prediction models of heating and cooling loads. In particular, this study proposes deep neural networks models based on several hyper-parameters: the number of hidden layers, the number of neurons in each layer, and the learning algorithm. The tuned models are constructed using a dataset generated with the Integrated Environmental Solutions Virtual Environment (IESVE) simulation software for the city of Buraydah city, the capital of the Qassim region in Saudi Arabia. The Qassim region was selected because of its harsh arid climate of extremely cold winters and hot summers, which means that lot of energy is used up for cooling and heating of residential buildings. Through model tuning, optimal parameters of deep learning models are determined using the following performance measures: Mean Square Error (MSE), Root Mean Square Error (RMSE), Regression (R) values, and coefficient of determination (R2). The results obtained with the five-layer deep neural network model, with 20 neurons in each layer and the Levenberg–Marquardt algorithm, outperformed the results of the other models with a lower number of layers. This model achieved MSE of 0.0075, RMSE 0.087, R and R2 both as high as 0.99 in predicting the heating load and MSE of 0.245, RMSE of 0.495, R and R2 both as high as 0.99 in predicting the cooling load. As the developed prediction models were based on buildings characteristics, the outcomes of the research may be relevant to architects at the pre-design stage of heating and cooling energy-efficient buildings.

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Section: Related Workmentioning

confidence: 99%

Tuning Deep Neural Networks for Predicting Energy Consumption in Arid Climate Based on Buildings Characteristics

et al. 2021

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“…In the same way, the methodology developed in [31] is based on Galerkin's formulation of the finite element method, to obtain representations of the relationship shown in Equation (1). Recently, a numerical methodology called the octahedric regression has been developed [32]. The octahedric regression can be considered as a hybrid method, which includes characteristics from the finite element method, radial basis function, and nearest neighbours.…”

Section: Mathematical Modellingmentioning

confidence: 99%

Modelling Faba Bean (Vicia faba L.) Biomass Production for Sustainability of Agricultural Systems of Pampas

et al. 2020

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The Pampas region is characterized by a high complexity in its productive system planning and faces the challenge of satisfying future food demands, as well as reducing the environmental impact of the activity. Climate change affects crops and farmers should use species capable of adapting to the changed climate. Among these species, faba bean (Vicia faba L.) cv. ‘Alameda’ has shown good adaptation to weather variability and, as a winter legume, it can help maintain the sustainability of agricultural systems in the area. The main purpose of this research was to select the models which describe the production characteristics of the ‘Alameda’ bean by using the least number of variables. Experimental and agrometeorological data from the cultivation of the ‘Alameda’ in Azul, Buenos Aires province, Argentina were used to generate mathematical models. Several modelling methodologies have been applied to study the production characteristics of the faba bean. The prediction of the models generated was analyzed by randomly disturbing the experimental data and analyzing the magnitude of the errors produced. The models obtained will be useful for predicting the biomass production of the faba bean cv. ‘Alameda’ grown in the agroclimatic conditions of Azul, Buenos Aires province, Argentina.

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“…Furthermore, the use of machine learning and deep learning models provide flexible and reliable solutions in this regard [ 4 ]. Several solutions exist for building energy prediction in the literature [ 5 , 6 , 7 ]. However, such solutions lack several aspects.…”

Section: Introductionmentioning

confidence: 99%

Building Heating and Cooling Load Prediction Using Ensemble Machine Learning Model

Chaganti

Rustam

Daghriri

et al. 2022

Sensors

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Building energy consumption prediction has become an important research problem within the context of sustainable homes and smart cities. Data-driven approaches have been regarded as the most suitable for integration into smart houses. With the wide deployment of IoT sensors, the data generated from these sensors can be used for modeling and forecasting energy consumption patterns. Existing studies lag in prediction accuracy and various attributes of buildings are not very well studied. This study follows a data-driven approach in this regard. The novelty of the paper lies in the fact that an ensemble model is proposed, which provides higher performance regarding cooling and heating load prediction. Moreover, the influence of different features on heating and cooling load is investigated. Experiments are performed by considering different features such as glazing area, orientation, height, relative compactness, roof area, surface area, and wall area. Results indicate that relative compactness, surface area, and wall area play a significant role in selecting the appropriate cooling and heating load for a building. The proposed model achieves 0.999 R2 for heating load prediction and 0.997 R2 for cooling load prediction, which is superior to existing state-of-the-art models. The precise prediction of heating and cooling load, can help engineers design energy-efficient buildings, especially in the context of future smart homes.

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An Octahedric Regression Model of Energy Efficiency on Residential Buildings

Cited by 9 publications

References 44 publications

Tuning Deep Neural Networks for Predicting Energy Consumption in Arid Climate Based on Buildings Characteristics

Tuning Deep Neural Networks for Predicting Energy Consumption in Arid Climate Based on Buildings Characteristics

Modelling Faba Bean (Vicia faba L.) Biomass Production for Sustainability of Agricultural Systems of Pampas

Building Heating and Cooling Load Prediction Using Ensemble Machine Learning Model

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