Development of Regression Models considering Time‐Lag and Aerosols for Predicting Heating Loads in Buildings

Lim, Hwan Sub; Kim, Gon

doi:10.1155/2018/4878021

Cited by 4 publications

(2 citation statements)

References 27 publications

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“…Shen et al [8] analyzed heat load performance based on simulation of neighborhood-scale building. Lim et al [9] proposed forecasting models for determining heat load with the consideration of the thermal inertia delay and buildings of varying size. Dahl et al [10] proposed an approach for improving short-term heat load forecasts with calendar and holiday data.…”

Section: Introductionmentioning

confidence: 99%

Forecasting the Heat Load of Residential Buildings with Heat Metering Based on CEEMDAN-SVR

Gao

Xue

et al. 2020

Energies

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The energy demand of the district heating system (DHS) occupies an important part in urban energy consumption, which has a great impact on the energy security and environmental protection of a city. With the gradual improvement of people’s economic conditions, different groups of people now have different demands for thermal energy for their comfort. Hence, heat metering has emerged as an imperative for billing purposes and sustainable management of energy consumption. Therefore, forecasting the heat load of buildings with heat metering on the demand side is an important management strategy for DHSs to meet end-users’ needs and maintain energy-saving regulations and safe operation. However, the non-linear and non-stationary characteristics of buildings’ heat load make it difficult to predict consumption patterns accurately, thereby limiting the capacity of the DHS to deliver on its statutory functions satisfactorily. A novel ensemble prediction model is proposed to resolve this problem, which integrates the advantages of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and support vector regression (SVR), called CEEMDAN-SVR in this paper. The proposed CEEMDAN-SVR algorithm is designed to automatically decompose the intrinsic mode according to the characteristics of heat load data to ensure an accurate representation of heat load patterns on multiple time scales. It will also be useful for developing an accurate prediction model for the buildings’ heat load. In formulating the CEEMDAN-SVR model, the heat load data of three different buildings in Xingtai City were acquired during the heating season of 2019–2020 and employed to conduct detailed comparative analysis with modern algorithms, such as extreme tree regression (ETR), forest tree regression (FTR), gradient boosting regression (GBR), support vector regression (SVR, with linear, poly, radial basis function (RBF) kernel), multi-layer perception (MLP) and EMD-SVR. Experimental results reveal that the performance of the proposed CEEMDAN-SVR model is better than the existing modern algorithms and it is, therefore, more suitable for modeling heat load forecasting.

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Section: Introductionmentioning

confidence: 99%

Forecasting the Heat Load of Residential Buildings with Heat Metering Based on CEEMDAN-SVR

Gao

Xue

et al. 2020

Energies

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“…An existing office building in Kermanshah, Iran was selected to implement and evaluate this proposed optimization model. H. S. Lim and G. Kim [27] developed models that can predict thermal loads for heating, taking into account the time phenomenon, using weather data collected by the Korea Meteorological Agency over a three-year period (2011-2014). In addition, different estimation models are developed for different sized buildings in the study.…”

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confidence: 99%

Modeling the relationship between outdoor meteorological data and energy consumptions at heating and cooling periods: Application in a university building

Kon

Yüksel

Karaoğlan

2024

NACO

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<p style='text-indent:20px;'>In this study regression modeling is proposed for calculating the heating and cooling load of the buildings by considering the outdoor meteorological data. For this pupose, Balikesir University Rectorate Building is selected as the case building. In the winter months, measurements were made in the hot water boiler in the basement of the building for the heating energy load. For the cooling energy load, measurements were made in the chiller groups near the building. As climate data, eight variables were considered: outdoor temperature, solar radiation, relative humidity, wind speed, atmospheric pressure, sunshine duration, steam pressure, and 1 m underground temperature. The Minitab statistical analysis program was used to perform the modeling. 55 samples were used for mathematically modeling the heating load, while 37 samples are used for modeling the cooling load. The R<inline-formula><tex-math id="M1">\begin{document}$ {}^{2\ } $\end{document}</tex-math></inline-formula>(coefficient of determination) values are calculated as 96.2% and 98.94% for cooling load and heating load, respectively. In addition to these findings, ANOVA results for both models were examined and both models were found to be significant.</p>

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The Observational Socio-Economic Study and Impact on the International Innovative Leadership in the United States

Kasztelnik

Brown

2020

SEC

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This paper summarizes how socio-economic study has impact on the international innovative leadership in the United States. This article includes the review will focus and examine literature and theories related to public policy organizational effectiveness as well as diversity leadership. Online databases including search engines located relevant books, journals, articles, and dissertations. The search terms used were descriptive of various themes such as diversity, diversity leadership, public policy and public policy leadership, organizational leadership, public policy and leadership, cultural diversity, organizational effectiveness, and public policy effectiveness. Additionally, measurements of public policy organizations effectiveness have been narrowly focused on past practice failing to include multiple organizational aspects similar to other professional organizations. Prior studies have been conducted in relationship to the effects of diversity leadership and leadership although related solely to the individual officer or the public policy organization. Notwithstanding, studies have been sparse which suggest that diversity leadership initiatives or enhanced leadership, increase public effectiveness, let alone collectively. Limited studies are not the case concerning public policy. This study attempted to fill this gap by examining if leadership of public policy outcomes of diversity leadership initiatives and the level of leadership predicts public policy organizational effectiveness. All of these terms have assisted in locating contemporary and relevant research. The results of the critical review article with analysis both authors can be useful for any leaderships around the World to support social-economics changes. Keywords: socio-economic, innovation, leadership, education, public policy.

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Development of Regression Models considering Time‐Lag and Aerosols for Predicting Heating Loads in Buildings

Cited by 4 publications

References 27 publications

Forecasting the Heat Load of Residential Buildings with Heat Metering Based on CEEMDAN-SVR

Forecasting the Heat Load of Residential Buildings with Heat Metering Based on CEEMDAN-SVR

Modeling the relationship between outdoor meteorological data and energy consumptions at heating and cooling periods: Application in a university building

The Observational Socio-Economic Study and Impact on the International Innovative Leadership in the United States

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