A relevant data selection method for energy consumption prediction of low energy building based on support vector machine

Paudel, Subodh; Elmitri, Mohamed; Couturier, Stéphane; Nguyen, Phuong H.; Kamphuis, René; Lacarrière, Bruno; Corre, Olivier Le

doi:10.1016/j.enbuild.2016.11.009

Cited by 136 publications

(48 citation statements)

References 25 publications

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“…Although there have not been as many studies utilizing machine learning in the building energy sector as there have been in other fields of research, there has been much research on building energy consumption predictions and analytical studies that utilize machine learning. Studies by Paudel et al, [38] Yildiz et al, [39] and Rahman et al [40] used machine learning to predict the energy consumption in buildings. Table 2 shows the target buildings and evaluation indexes for predicting building energy use.…”

Section: Analysis Of Building Energy Consumption Using Machine Learningmentioning

confidence: 99%

Investigating Primary Factors Affecting Electricity Consumption in Non-Residential Buildings Using a Data-Driven Approach

et al. 2019

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Although the latest energy-efficient buildings use a large number of sensors and measuring instruments to predict consumption more accurately, it is generally not possible to identify which data are the most valuable or key for analysis among the tens of thousands of data points. This study selected the electric energy as a subset of total building energy consumption because it accounts for more than 65% of the total building energy consumption, and identified the variables that contribute to electric energy use. However, this study aimed to confirm data from a building using clustering in machine learning, instead of a calculation method from engineering simulation, to examine the variables that were identified and determine whether these variables had a strong correlation with energy consumption. Three different methods confirmed that the major variables related to electric energy consumption were significant. This research has significance because it was able to identify the factors in electric energy, accounting for more than half of the total building energy consumption, that had a major effect on energy consumption and revealed that these key variables alone, not the default values of many different items in simulation analysis, can ensure the reliable prediction of energy consumption.

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Section: Analysis Of Building Energy Consumption Using Machine Learningmentioning

confidence: 99%

Investigating Primary Factors Affecting Electricity Consumption in Non-Residential Buildings Using a Data-Driven Approach

et al. 2019

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“…Thermal overall heat transfer coefficient (U-Value) for external walls can be one of the leading ways to determine the cooling load in buildings. Reducing the heat transfer coefficient leads to reduce the transferred heat by conduction and consequently decreases the energy demand [26]. This can be achieved by using low heat transfer construction materials in the building's envelope.…”

Section: ) Overall Heat Transfer Coefficientmentioning

confidence: 99%

Prediction of Hourly Cooling Energy Consumption of Educational Buildings Using Artificial Neural Network

Jaber¹,

Saleh²,

Ali³

2019

Int. J. Adv. Sci. Eng. Inf. Technol.

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Predicating the required building energy when it is in the design stage and before being constructed considers a crucial step for in charge people. Hence, the main aim of this research is to accurately forecast the needed building cooling energy per hour for educational buildings at University of Technology in Iraq. For this purpose, the feed forward artificial neural network (ANN) has been selected as an efficient technique to develop such a predication system. Firstly, the main building parameters have been investigated and then only the most important ones were chosen to be used as inputs to the ANN model. However, due to the long time period that is required to collect actual consumed building energy in order to be employed for ANN model training, the hourly analysis program (HAP), which is a building simulation software, has been utilized to produce a database covering the summer months in Iraq. Different training algorithms and range of learning rate values have been investigated, and the Bayesian regularization backpropagation training algorithm and 0.05 learning rate were found very suitable for precise cooling energy prediction. To evaluate the performance of the optimized ANN model, mean square error (MSE) and correlation coefficient (R) have been adopted. The MSE and R indices for the predication results proved that the optimized ANN model is having a high predication accuracy with 5.99*10-6 and 0.9994, respectively.

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“…The physical and semi-physical methods estimate the energy demand of a building from geometrical information and thermal properties of the building [7]. However, the physical and semi-physical methods require a detailed understanding of building thermal dynamics to obtain several physical parameters [8]. By contrast, data-driven methods have been widely used to construct the air-conditioning energy consumption models due to their great processing capacity in solving nonlinear problems.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Air-Conditioning Energy Consumption in R&D Building Using Multiple Machine Learning Techniques

Liao

Chang

2020

Energies

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With the global increase in demand for energy, energy conservation of research and development buildings has become of primary importance for building owners. Knowledge based on the patterns in energy consumption of previous years could be used to predict the near-future energy usage of buildings, to optimize and facilitate more effective energy consumption. Hence, this research aimed to develop a generic model for predicting energy consumption. Air-conditioning was used to exemplify the generic model for electricity consumption, as it is the process that often consumes the most energy in a public building. The purpose of this paper is to present this model and the related findings. After causative factors were determined, the methods of linear regression and various machine learning techniques—including the earlier machine learning techniques of support vector machine, random forest, and multilayer perceptron, and the later machine learning techniques of deep neural network, recurrent neural network, long short-term memory, and gated recurrent unit—were applied for prediction. Among them, the prediction of random forest resulted in an R2 of 88% ahead of the first month and 81% ahead of the third month. These experimental results demonstrate that the prediction model is reliable and significantly accurate. Building owners could further enrich the model for energy conservation and management.

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A relevant data selection method for energy consumption prediction of low energy building based on support vector machine

Cited by 136 publications

References 25 publications

Investigating Primary Factors Affecting Electricity Consumption in Non-Residential Buildings Using a Data-Driven Approach

Investigating Primary Factors Affecting Electricity Consumption in Non-Residential Buildings Using a Data-Driven Approach

Prediction of Hourly Cooling Energy Consumption of Educational Buildings Using Artificial Neural Network

Prediction of Air-Conditioning Energy Consumption in R&D Building Using Multiple Machine Learning Techniques

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