Exploring the potential of tree-based ensemble methods in solar radiation modeling

Hassan, Muhammed A.; Khalil, A.; Kaseb, S.; Kassem, Mohamed A.

doi:10.1016/j.apenergy.2017.06.104

Cited by 157 publications

(68 citation statements)

References 48 publications

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“…XGBoost is an extension of the Gradient Boosting Machine. The Boosting classifier belongs to the ensemble learning model (Hassan et al 2017;Urraca et al 2017). XGBoost is widely used in energy consumption prediction (Touzani et al 2018;Robinson et al 2017) and power distribution due to its high efficiency and accuracy.…”

Section: Evaluation Modelmentioning

confidence: 99%

“…However, the sample size was usually limited, and the existing methods greatly reduced the accuracy of the evaluation formula (Deng et al 2018;Jin et al 2017). In order to solve the above problems, the real store scene was built in the Key Laboratory of Building Environment Simulation to conduct an evaluation experiment; XGBoost (Hassan et al 2017;Urraca et al 2017) was used to solve the classification problem of big data. Based on this, the impact of lighting parameters on the lighting quality were studied, and lighting evaluation models were established to ensure visual health, to improve visual comfort, and to save lighting energy consumption, while satisfying the functions of lighting.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Lighting Quality Study of Shopping Malls in China Based on the Evaluation Experiment

Dang¹,

Bu²,

Wang³

et al. 2018

Healthy, Intelligent and Resilient Buildings and Urban Environments

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The lighting environment of the shopping malls will greatly influence the customer's visual comfort and product sales. This experiment studied the lighting environment of jewelry stores with the highest lighting energy consumption in shopping malls. The jewelry store scene which was built in the Key Laboratory of Building Environment Simulation had a total of 150 lighting conditions. Then, we measured lighting parameters, and subjective evaluation of lighting quality (including lighting comfort and commodities display effect) with 30 participants were carried out. We analyzed the experimental data to get the influence law of lighting parameters on lighting quality. Finally, this paper introduces an emerging ensemble learning algorithm XGBoost (extreme gradient boosting) to construct evaluation models of multiple lighting parameters for lighting quality in jewelry stores, which were verified to have good accuracy. It is of great significance to save energy on the basis of ensuring the lighting quality of the shopping malls.

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Section: Evaluation Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Lighting Quality Study of Shopping Malls in China Based on the Evaluation Experiment

Dang¹,

Bu²,

Wang³

et al. 2018

Healthy, Intelligent and Resilient Buildings and Urban Environments

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“…This is owing to problems related to non-renewable energies, a lack of other energy sources, increasing the use of energy and potential availability of solar energy [1][2][3][4]. Stations with long historical measurements of GHI are limited because of the cost of installation and maintenance, and issues related to the pyranometers [5]. Therefore, several studies have tried to estimate GHI empirically from the early 20th century until now from other climate variables, namely, Sunshine Duration (SD), Air Temperature (AT), cloud cover, and other variables, using the top-of-atmosphere irradiance on the horizontal surface (TOA) [6][7][8][9][10][11] and with linear regression models [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Modelling Hourly Global Horizontal Irradiance from Satellite-Derived Datasets and Climate Variables as New Inputs with Artificial Neural Networks

et al. 2019

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More accurate data of hourly Global Horizontal Irradiance (GHI) are required in the field of solar energy in areas with limited ground measurements. The aim of the research was to obtain more precise and accurate hourly GHI by using new input from Satellite-Derived Datasets (SDDs) with new input combinations of clear sky (Cs) and top-of-atmosphere (TOA) irradiance on the horizontal surface and with observed climate variables, namely Sunshine Duration (SD), Air Temperature (AT), Relative Humidity (RH) and Wind Speed (WS). The variables were placed in ten different sets as models in an artificial neural network with the Levenberg–Marquardt training algorithm to obtain results from training, validation and test data. It was applied at two station types in northeast Iraq. The test data results with observed input variables (correlation coefficient (r) = 0.755, Root Mean Square Error (RMSE) = 33.7% and bias = 0.3%) are improved with new input combinations for all variables (r = 0.983, RMSE = 9.5% and bias = 0.0%) at four automatic stations. Similarly, they improved at five tower stations with no recorded SD (from: r = 0.601, RMSE = 41% and bias = 0.7% to: r = 0.976, RMSE = 11.2% and bias = 0.0%). The estimation of hourly GHI is slightly enhanced by using the new inputs.

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“…These methods are evolutionary computation techniques that can achieve the best relationship in a system with data driving tool. Due to the capability of the ML methods in tackling non-linear relationship between dependent and independent variables [15], numerous ML techniques have been applied and proposed to predict ETo for agricultural purposes including genetic programming (GP) [16,17], kernel based algorithms, e,g, support vector machine (SVM) [18,19], artificial neural network [20][21][22], wavelet neural network [13,23], random forest (RF) [24,25], and multiple linear regression (LR) [26,27].…”

Section: Introductionmentioning

confidence: 99%

“…Due to the high computational costs and complexity of the ML models, the tree-based ensemble models attract people by its simplicity and estimation power. The RF model as a ML tree-based model is able to produce a great result compared to the other ML models [24,38]. This model known for its simplicity and the ability for both classification and regression tasks.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Machine Learning Techniques for Daily Reference Evapotranspiration Estimation

Niaghi¹,

Hassanijalilian²,

Shiri³

2019

Preprint

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The ASCE-EWRI reference evapotranspiration (ETo) equation is recommended as a standardized method for reference crop ETo estimation. However, various climate data as input variables to the standardized ETo method are considered limiting factors in most cases and restrict the ETo estimation. This paper assessed the potential of different machine learning (ML) models for ETo estimation using limited meteorological data. The ML models used to estimate daily ETo included Gene Expression Programming (GEP), Support Vector Machine (SVM), Multiple Linear Regression (LR), and Random Forest (RF). Three input combinations of daily maximum and minimum temperature (Tmax and Tmin), wind speed (W) with Tmax and Tmin, and solar radiation (Rs) with Tmax and Tmin were considered using meteorological data during 2003-2016 from six weather stations in the Red River Valley. To understand the performance of the applied models with the various combinations, station, and yearly based tests were assessed with local and spatial approaches. Considering the local and spatial approaches analysis, the LR and RF models illustrated the lowest rate of improvement compared to GEP and SVM. The spatial RF and SVM approaches showed the lowest and highest values of the scatter index as 0.333 and 0.457, respectively. As a result, the radiation-based combination and the RF model showed the best performance with higher accuracy for all stations either locally or spatially, and the spatial SVM and GEP illustrated the lowest performance among models and approaches.

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Exploring the potential of tree-based ensemble methods in solar radiation modeling

Cited by 157 publications

References 48 publications

Lighting Quality Study of Shopping Malls in China Based on the Evaluation Experiment

Lighting Quality Study of Shopping Malls in China Based on the Evaluation Experiment

Modelling Hourly Global Horizontal Irradiance from Satellite-Derived Datasets and Climate Variables as New Inputs with Artificial Neural Networks

Evaluation of Machine Learning Techniques for Daily Reference Evapotranspiration Estimation

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