scholarly journals Forecasting Japan’s Solar Energy Consumption Using a Novel Incomplete Gamma Grey Model

2019 ◽  
Vol 11 (21) ◽  
pp. 5921 ◽  
Author(s):  
Peng Zhang ◽  
Xin Ma ◽  
Kun She
Keyword(s):  
Energy Consumption ◽  
Solar Energy ◽  
Nonlinear Coefficient ◽  
Prediction Method ◽  
Decision Makers ◽  
Grey Model ◽  
Correct Prediction ◽  
Grey Prediction ◽  
Real World Datasets ◽  
Application Fields

Energy consumption is an essential basis for formulating energy policy and programming, especially in the transition of energy consumption structure in a country. Correct prediction of energy consumption can provide effective reference data for decision-makers and planners to achieve sustainable energy development. Grey prediction method is one of the most effective approaches to handle the problem with a small amount of historical data. However, there is still room to improve the prediction performance and enlarge the application fields of the traditional grey model. Nonlinear grey action quantity can effectively improve the performance of the grey prediction model. Therefore, this paper proposes a novel incomplete gamma grey model (IGGM) with a nonlinear grey input over time. The grey input of the IGGM model is a revised incomplete gamma function of time in which the nonlinear coefficient determines the performance of the IGGM model. The WOA algorithm is employed to seek for the optimal incomplete coefficient of the IGGM model. Then, the validations of IGGM are performed on four real-world datasets, and the results exhibit that the IGGM model has more advantages than the other state-of-the-art grey models. Finally, the IGGM model is applied to forecast Japan’s solar energy consumption in the next three years.

Research on Data Prediction Methods of Structural Health Monitoring System

2012 ◽  
Vol 256-259 ◽  
pp. 1022-1028
Author(s):  
Chun Xiao ◽  
Xue Ping Hao ◽  
Li Qiao Li ◽  
Wei Li ◽  
Xun Gang Liu
Keyword(s):  
Structural Health Monitoring ◽  
Monitoring System ◽  
Health Monitoring ◽  
Model Prediction ◽  
Prediction Method ◽  
Grey Model ◽  
Prediction Methods ◽  
Grey Prediction ◽  
Health Monitoring System ◽  
Term Data

Trend prediction is virtually modeling process for dynamic data. The key to prediction is to establish a model in accordance with actual status, then use the model to predict the trend of object, and infer its behavior in future. Two prediction methods are researched to predict the trend on the observed points of the structure in this paper, which are regression prediction method and grey prediction method. The continuous time strain value of a measured point on Tianxingzhou Yangtze River Bridge is used as data sample for researching. The method of regression analysis is applied for predicting the trend of short-term data, and the method of grey model prediction for predicting long-term data. Regression prediction can assess the health status of the structure and obtain the alarm information effectively by comparing the actual monitoring data with the range of forecast interval. Grey prediction method has great advantages when dealing with poor information. By engineering example this study shows the pros and cons of these two methods, and proves that the method of grey model prediction is more suitable of predicting the trend of object in the structural health monitoring system.

GM (1,1) Model Application in Power Load Forecasting Based on Sliding Average Method

2014 ◽  
Vol 687-691 ◽  
pp. 1300-1303
Author(s):  
Li Zhi Song
Keyword(s):  
Average Method ◽  
Moving Average ◽  
Load Forecasting ◽  
Prediction Method ◽  
Grey Model ◽  
Grey Prediction ◽  
Model Application ◽  
Dispersion Degree ◽  
Power Load ◽  
Power Load Forecasting

The grey prediction method is simple in principle, the sample size was small and simple, suitable for load forecasting.But grey model has some limitations, the data dispersion degree is more bigger,the gray is also more bigger, it will reduce the accuracy of prediction.This paper adopts the moving average method to improve the raw data , so as to increase the data weights, while avoiding predicted value excessive volatility .Through a city of China's power load is instantiated to verify, and Then analyze the results, found that after the GM (1,1) model improved by moving average method can effectively improve the accuracy of load forecasting.

scholarly journals A Novel Power-Driven Grey Model with Whale Optimization Algorithm and Its Application in Forecasting the Residential Energy Consumption in China

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-22 ◽  
Author(s):  
Peng Zhang ◽  
Xin Ma ◽  
Kun She
Keyword(s):  
Energy Consumption ◽  
Optimization Algorithm ◽  
Nonlinear Parameter ◽  
Whale Optimization Algorithm ◽  
Grey Model ◽  
Living Standard ◽  
Grey Prediction ◽  
Residential Energy ◽  
Residential Energy Consumption ◽  
Whale Optimization

Along with the improvement of Chinese people’s living standard, the proportion of residential energy consumption in total energy consumption is rapidly increasing in China year by year. Accurately forecasting the residential energy consumption is conducive to making energy programming and supply plan for the administrative departments or energy companies. By improving the grey action quantity of traditional grey model with an exponential time term, a novel power-driven grey model is proposed to forecast energy consumption as reference data for decision makers. The nonlinear parameter of power-driven grey action quantity is a crucial factor to influence the prediction precision. To promote the prediction accuracy of the power-driven grey model, whale optimization algorithm is adopted to seek for the optimal value of the nonlinear parameter. Two validations on real-world datasets are conducted, and the results indicate that the power-driven grey model has significant advantages on the aspect of prediction performance compared with the other seven classical grey prediction methods. Finally, the power-driven grey model is applied in forecasting the total residential energy and the thermal energy consumption of China.

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.

scholarly journals Forecasting Solar Energy Consumption Using A Fractional Discrete Grey Model With Time Power Term

2021 ◽  
Author(s):  
Huiping Wang ◽  
Yi Wang
Keyword(s):  
Energy Consumption ◽  
Solar Energy ◽  
South Korea ◽  
Prediction Model ◽  
Asia Pacific ◽  
Grey Wolf Optimizer ◽  
Pacific Region ◽  
Grey Prediction ◽  
Asia Pacific Region ◽  
Grey Prediction Model

Abstract Accurate prediction of energy consumption is an important basis for policymakers to formulate and improve energy policies and measures. In this paper, a new grey prediction model FDGM(1,1, tα ) is proposed. The grey wolf optimizer (GWO) is used to optimize the fractional-order r and the time power α in the model. A numerical example and four sets of solar energy consumption data (France, South Korea, OECD, and Asia Pacific region) are used to establish the FDGM(1,1, tα ) model. Based on the idea of metabolism, the solar energy consumption of the above four economies in the next 10 years is predicted. The results show that the FDGM(1,1, tα ) model is more reliable and effective than the other seven grey models. From 2020 to 2029, the solar energy consumption in South Korea, the OECD, and the Asia Pacific region will gradually increase; the solar energy consumption in France will slowly increase in the next few years and will gradually decrease after reaching a peak in 2026. The grey prediction model FDGM(1,1, tα ) proposed in this paper has strong adaptability and can be used not only for the prediction of solar energy consumption but also for the prediction of other energy sources.

scholarly journals Forecasting China’s per Capita Living Energy Consumption by Employing a Novel DGM (1, 1, tα) Model with Fractional Order Accumulation

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yumu Lu ◽  
Chong Liu ◽  
Haodan Pang ◽  
Ting Feng ◽  
Zijie Dong
Keyword(s):  
Energy Consumption ◽  
Fractional Order ◽  
Prediction Models ◽  
Grey Model ◽  
Grey Prediction ◽  
Grey System ◽  
Annual Increment ◽  
Model Based ◽  
Proposed Model ◽  
Per Capita

The living energy consumption of residents has become an important technical index to promote the economic and social development strategy. The country’s medium- and short-term living energy consumption is featured with both a certainty of annual increment and an uncertainty of random variation. Thus, it can be seen as a typical grey system and shall be suitable for the grey prediction model. In order to explore the future development trend of China’s per capita living energy consumption, this paper establishes a novel grey model based on the discrete grey model with time power term and the fractional accumulation (FDGM (1, 1, tα) for short) for forecasting China’s per capita living energy consumption, which makes the existing model to adapt to different time series by adjusting fractional order accumulation parameter and power term. In order to verify the feasibility and effectiveness of the novel model, the proposed and eight other existing grey prediction models are applied to the case of China’s per capita living energy consumption. The results show that the proposed model is more suitable for predicting China’s per capita energy consumption than the other eight grey prediction models. Finally, the proposed model based on metabolism mechanism is used to predict China’s per capita living energy consumption from 2018 to 2029, which can provide a reference for energy companies or government decision makers.

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