Critique of operating variables importance on chiller energy performance using random forest

Yu, F.W.; Ho, W.T.; Chan, K.T.; Sit, R.K.Y.

doi:10.1016/j.enbuild.2017.01.063

Cited by 32 publications

(7 citation statements)

References 18 publications

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“…The RF model has been widely used in variable importance studies by researchers from various fields (Li et al 2020a(Li et al , 2020bYi Li et al 2018;D. Liu et al 2020;Yu et al 2017)and considered one of the most accurate model for regression and classification (Ardestani et al 2014). Niu et al (2020) used RF to construct the index of urban poverty in Guangzhou.…”

Section: Introductionmentioning

confidence: 99%

Socioeconomic and environmental factors of poverty in China using geographically weighted random forest regression model

Luo

Yan

McClure

et al. 2022

Environ Sci Pollut Res

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Correlations between socioeconomic factors and poverty in regression models do not reflect actual relationships, especially when data exhibit patterns of spatial heterogeneity. Spatial regression models can estimate the relationships between socioeconomic factors and poverty in defined geographical areas, explaining the imbalanced distribution of poverty, but the relationships between these factors and poverty are not always linear however, and conventional simple linear local regression models do not accurately capture these nonlinear relationships. To fill this gap, we used a local regression method, geographically weighted random forest regression (GW-RFR), that integrates a spatial weight matrix (SWM) and random forest (RF). The GW-RFR evaluates the spatial variations in the nonlinear relationships between variables. A county-level poverty data set of China was employed to estimate the performance of the GW-RFR against the random forest (RF). In this poverty application, the value of was 0.128 higher than that of the RF, the NRMSE value was 1.6% lower than the RF, and the MAE value was 0.295 lower than the RF. These results showed that the relationship between poverty factors and poverty varies with space at the county level in China, and the GW-RFR was suitable for dealing with nonlinear relationships in local regression analysis. Supplementary Information The online version contains supplementary material available at 10.1007/s11356-021-17513-3.

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Section: Introductionmentioning

confidence: 99%

Socioeconomic and environmental factors of poverty in China using geographically weighted random forest regression model

Luo

Yan

McClure

et al. 2022

Environ Sci Pollut Res

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“…It takes into account the complex influencing factors and many uncertainties of global offshore wind power projects. At present, many studies have proven that the random forest algorithm has the effect of identifying factors [40]. Richmond et al (2020) used random forest to identify key factors, and proposed a method of selecting input vectors of wind power forecast model based on random forest, which has a better comprehensive performance [41].…”

Section: Random Forest Algorithmmentioning

confidence: 99%

Forecast Research on Multidimensional Influencing Factors of Global Offshore Wind Power Investment Based on Random Forest and Elastic Net

Niu

et al. 2021

Sustainability

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Recently, countries around the world have begun to develop low-carbon energy sources to alleviate energy shortage and cope with climate change. The offshore wind power has become a new direction for clean energy exploration. However, the accuracy of offshore wind power investment is still an urgent problem due to its complexity. Therefore, this paper investigates offshore wind power investment to improve the investment forecasting accuracy. In this study, the random forest (RF) algorithm was used to screen out the key factors influencing multi-dimensional global offshore wind power investment, and the elastic net (EN) was optimized using the ADMM algorithm and used in the global offshore wind power investment forecast model. The results show that the adoption of the random forest algorithm can effectively screen out the key influencing factors of global offshore wind power investment. Water depth, offshore distance and sweeping area have the most influence on the investment. Moreover, compared with other models, the elastic net optimized by ADMM can better reflect the changing trend of global offshore wind power investment, with smaller errors and a higher regression accuracy. The application of the RF–EN combined model can screen out effective factors from complex multi-dimensional influencing factors, and perform high-precision regression analysis, which is conducive to improving the global offshore wind power investment forecast. The conclusion obtained can set a more reasonable plan for the future construction and investment of global offshore wind power projects.

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“…Neural networkbased methods are a common solution, as corroborated by a review of modeling methods for HVAC systems which shows several related applications [32]. However, coming up with reduced and generic sets of variables that allow the characterization of the performance in all cases is not feasible, which requires the methodology to be able to consider a large set of influencing variables [33]. Thus, deep learning methods, in particular, are especially well suited to solve this problem due to their capacity for feature learning [34].…”

Section: Performance Modeling Of Hvac Equipmentmentioning

confidence: 99%

Predictive chiller operation: A data-driven loading and scheduling approach

Cardoso

Delgado-Prieto

Kampouropoulos

et al. 2020

Energy and Buildings

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Critique of operating variables importance on chiller energy performance using random forest

Cited by 32 publications

References 18 publications

Socioeconomic and environmental factors of poverty in China using geographically weighted random forest regression model

Socioeconomic and environmental factors of poverty in China using geographically weighted random forest regression model

Forecast Research on Multidimensional Influencing Factors of Global Offshore Wind Power Investment Based on Random Forest and Elastic Net

Predictive chiller operation: A data-driven loading and scheduling approach

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