scholarly journals Predicting Building Energy Consumption with a New Grey Model

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Yan Zhang ◽  
Huiping Wang ◽  
Yi Wang
Keyword(s):  
Energy Consumption ◽  
Prediction Model ◽  
Building Energy ◽  
Grey Model ◽  
Annual Growth Rate ◽  
Grey Prediction ◽  
Building Energy Consumption ◽  
Total Energy Consumption ◽  
Grey Prediction Model ◽  
Grey Models

Based on the existing grey prediction model, this paper proposes a new grey prediction model (the fractional discrete grey model, FDGM (1, 1, t α )), introduces the modeling mechanism and characteristics of the FDGM (1, 1, t α ), and uses three groups of data to verify its effectiveness compared with that of other grey models. This paper forecasts the building energy consumption in China over the next five years based on the idea of metabolism. The results show that the FDGM (1, 1, t α ) can be transformed into other grey models through parameter setting changes, so the new model has strong adaptability. The FDGM (1, 1, t α ) is more reliable and effective than the other six compared grey models. From 2018 to 2022, the total energy consumption levels of civil buildings, urban civil buildings, and civil buildings specifically in Beijing will exhibit steady upward trends, with an average annual growth rate of 2.61%, 1.92%, and 0.78%, respectively.

Impact of Building Orientation and Window-Wall Ratio on the Office Building Energy Consumption

2013 ◽  
Vol 409-410 ◽  
pp. 606-611 ◽  
Author(s):  
Zhen Yu ◽  
Wei Lin Zhang ◽  
Ting Yong Fang
Keyword(s):  
Energy Consumption ◽  
Air Conditioning ◽  
Building Energy ◽  
Simulation Software ◽  
Office Building ◽  
Building Energy Consumption ◽  
Total Energy Consumption ◽  
Building Orientation ◽  
Heating Energy Consumption ◽  
Energy Consumption Simulation

Using the energy consumption simulation software to research the HVAC in fall air conditioning mode, different building orientation and window-wall ratio of the office building energy consumption. The study found that the heating energy consumption, air-conditioning energy consumption and total energy consumption is gradually increased with the increase of the window-wall ratio under the same orientation. The result provides some reference for public buildings in setting of building orientation and window-wall ratio.

scholarly journals Forecasting Chinese Wind Power Installed Capacity Using a Novel Grey Model with Parameters Combination Optimization

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Xiaoshuang Luo ◽  
Bo Zeng ◽  
Hui Li ◽  
Wenhao Zhou
Keyword(s):  
Prediction Model ◽  
Wind Power ◽  
Prediction Models ◽  
Grey Model ◽  
The Novel ◽  
Grey Prediction ◽  
Grey Prediction Model ◽  
Combination Optimization ◽  
Installed Capacity ◽  
Novel Model

The intermittent and uncertain characteristics of wind generation have brought new challenges for the hosting capacity and the integration of large-scale wind power into the power system. Consequently, reasonable forecasting wind power installed capacity (WPIC) is the most effective and applicable solution to meet this challenge. However, the single parameter optimization of the conventional grey model has some limitations in improving its modeling ability. To this end, a novel grey prediction model with parameters combination optimization is proposed in this paper. Firstly, considering the modeling mechanism and process, the order of accumulation generation of the grey prediction model is optimized by Particle Swarm Optimization (PSO) Algorithm. Secondly, as different orders of accumulation generation correspond to different parameter matrixes, the background value coefficient of the grey prediction model is optimized based on the optimal accumulation order. Finally, the novel model of combinational optimization is employed to simulate and forecast Chinese WPIC, and the comprehensive error of the novel model is only 1.34%, which is superior to the other three grey prediction models (2.82%, 1.68%, and 2.60%, respectively). The forecast shows that China’s WPIC will keep growing in the next five years, and some reasonable suggestions are put forward from the standpoint of the practitioners and governments.

scholarly journals Calculation and realization of new method grey residual error correction model

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254154
Author(s):  
Lifang Xiao ◽  
Xiangyang Chen ◽  
Hao Wang
Keyword(s):  
Prediction Model ◽  
Least Square Method ◽  
Residual Error ◽  
Least Square ◽  
Grey Model ◽  
Grey Prediction ◽  
Grey Prediction Model ◽  
True Value ◽  
Prediction Function ◽  
Fitting Method

Aiming at the problem of prediction accuracy of stochastic volatility series, this paper proposes a method to optimize the grey model(GM(1,1)) from the perspective of residual error. In this study, a new fitting method is firstly used, which combines the wavelet function basis and the least square method to fit the residual data of the true value and the predicted value of the grey model(GM(1,1)). The residual prediction function is constructed by using the fitting method. Then, the prediction function of the grey model(GM(1,1)) is modified by the residual prediction function. Finally, an example of the wavelet residual-corrected grey prediction model (WGM) is obtained. The test results show that the fitting accuracy of the wavelet residual-corrected grey prediction model has irreplaceable advantages.

A novel adaptive discrete grey prediction model for forecasting development in energy consumption structure—from the perspective of compositional data

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Wuyong Qian ◽  
Hao Zhang ◽  
Aodi Sui ◽  
Yuhong Wang
Keyword(s):  
Energy Consumption ◽  
Prediction Model ◽  
Compositional Data ◽  
Development Trend ◽  
Grey Prediction ◽  
Content Type ◽  
Consumption Structure ◽  
Grey Prediction Model ◽  
Proposed Model

PurposeThe purpose of this study is to make a prediction of China's energy consumption structure from the perspective of compositional data and construct a novel grey model for forecasting compositional data.Design/methodology/approachDue to the existing grey prediction model based on compositional data cannot effectively excavate the evolution law of correlation dimension sequence of compositional data. Thus, the adaptive discrete grey prediction model with innovation term based on compositional data is proposed to forecast the integral structure of China's energy consumption. The prediction results from the new model are then compared with three existing approaches and the comparison results indicate that the proposed model generally outperforms existing methods. A further prediction of China's energy consumption structure is conducted into a future horizon from 2021 to 2035 by using the model.FindingsChina's energy structure will change significantly in the medium and long term and China's energy consumption structure can reach the long-term goal. Besides, the proposed model can better mine and predict the development trend of single time series after the transformation of compositional data.Originality/valueThe paper considers the dynamic change of grey action quantity, the characteristics of compositional data and the impact of new information about the system itself on the current system development trend and proposes a novel adaptive discrete grey prediction model with innovation term based on compositional data, which fills the gap in previous studies.

Building Energy Consumption Prediction of Housing Industry in China Based on Hybrid Models

2011 ◽  
Vol 201-203 ◽  
pp. 2466-2469 ◽  
Author(s):  
Ying Xie
Keyword(s):  
Energy Consumption ◽  
Building Energy ◽  
Hybrid Models ◽  
Support Vector ◽  
National Bureau ◽  
Building Energy Consumption ◽  
Total Energy Consumption ◽  
Consumption Index ◽  
Housing Industry ◽  
Energy Consumption Prediction

Building energy consumption is a vital part of the total energy consumption in China, it is meaningful to predict the building energy consumption exactly as it is useful in the effective implementation of energy policies and is propitious for further expansion of the housing industry. In this paper, based on the factor analysis theory to reduce the dimension of the building energy consumption index, hybrid models of BP neural network and Least Squares Support Vector Machines are constructed respectively to predict the building energy consumption. Relevant data is collected from National Bureau of Statistics of China (1981~2009). Data analysis shows the proposed models, especially based on LS-SVMs, have more steady performance and higher accuracy.

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