Research on China's Energy Demand Forecasting Based on Grey System Theory

2012 ◽  
Vol 524-527 ◽  
pp. 2958-2961
Author(s):  
Li Jie Wang ◽  
Jun Wei Xu ◽  
Pei Qin
Keyword(s):  
Energy Consumption ◽  
Energy Demand ◽  
Demand Forecasting ◽  
Grey System Theory ◽  
System Model ◽  
Practical Significance ◽  
Annual Data ◽  
Grey System ◽  
Gray System ◽  
Energy Demand Forecasting

This paper is based on the energy consumption annual data of China's 1990-2010 and uses the improved gray system model GM (1,1, α) for analyzing and testing, in order to determine the range of α values of the model. Thus, the range of data can be applied for predicting and analyzing the energy consumption of the next decade. It can be seen from the results that the model has a reasonable and practical significance. Finally, this paper summarizes the application of the model.

Profiling of Prosumers for the Needs of Electric Energy Demand Estimation in Microgrids

2015 ◽  
Vol 4 (4) ◽  
pp. 29-45 ◽  
Author(s):  
Karol Fabisz ◽  
Agata Filipowska ◽  
Tymoteusz Hossa
Keyword(s):  
Energy Consumption ◽  
Smart Grids ◽  
Energy Demand ◽  
Additional Data ◽  
Demand Forecasting ◽  
Electric Energy ◽  
Demand Estimation ◽  
Ex Post ◽  
Or Groups ◽  
Energy Demand Forecasting

Nowadays, a lot of attention regarding smart grids' development is devoted to delivery of methods for estimation of the energy demand taking into account the behavior of network participants (being single prosumers or groups of prosumers). These methods take an advantage from an analysis of the ex-post data on energy consumption, usually with no additional data about profiles of prosumers. The goal of this paper is to present and validate a method for an energy demand forecasting based on profiling of prosumers that enables estimation of the energy demand for every user stereotype, every hour, every day of the year and even for every device. The paper presents possible scenarios on how the proposed approach can be used for the benefit of the microgrid.

scholarly journals Energy Demand Forecasting: Combining Cointegration Analysis and Artificial Intelligence Algorithm

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Junbing Huang ◽  
Yuee Tang ◽  
Shuxing Chen
Keyword(s):  
Artificial Intelligence ◽  
Quadratic Forms ◽  
Energy Demand ◽  
Energy Use ◽  
Demand Forecasting ◽  
Cointegration Analysis ◽  
Adaptive Genetic Algorithm ◽  
Annual Data ◽  
Annual Growth Rate ◽  
Energy Demand Forecasting

Energy is vital for the sustainable development of China. Accurate forecasts of annual energy demand are essential to schedule energy supply and provide valuable suggestions for developing related industries. In the existing literature on energy use prediction, the artificial intelligence-based (AI-based) model has received considerable attention. However, few econometric and statistical evidences exist that can prove the reliability of the current AI-based model, an area that still needs to be addressed. In this study, a new energy demand forecasting framework is presented at first. On the basis of historical annual data of electricity usage over the period of 1985–2015, the coefficients of linear and quadratic forms of the AI-based model are optimized by combining an adaptive genetic algorithm and a cointegration analysis shown as an example. Prediction results of the proposed model indicate that the annual growth rate of electricity demand in China will slow down. However, China will continue to demand about 13 trillion kilowatt hours in 2030 because of population growth, economic growth, and urbanization. In addition, the model has greater accuracy and reliability compared with other single optimization methods.

scholarly journals Urban integrated energy demand forecasting method considering multi-station fusion

2021 ◽  
Vol 651 (2) ◽  
pp. 022084
Author(s):  
Haoyu Wu ◽  
Jiaxin Ma ◽  
Chunyan Zhang ◽  
Hua Zhou ◽  
Shimin Bian ◽  
...  
Keyword(s):  
Energy Demand ◽  
Demand Forecasting ◽  
Forecasting Method ◽  
Energy Demand Forecasting

scholarly journals Usage of the Pareto Fronts as a Tool to Select Data in the Forecasting Process—A Short-Term Electric Energy Demand Forecasting Case

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3204
Author(s):  
Michał Sabat ◽  
Dariusz Baczyński
Keyword(s):  
Energy Demand ◽  
Demand Forecasting ◽  
Electric Energy ◽  
Forecasting Model ◽  
Short Term ◽  
Complex Evaluation ◽  
Proposed Model ◽  
Energy Demand Forecasting ◽  
Pareto Fronts ◽  
Naive Approach

Transmission, distribution, and micro-grid system operators are struggling with the increasing number of renewables and the changing nature of energy demand. This necessitates the use of prognostic methods based on ever shorter time series. This study depicted an attempt to develop an appropriate method by introducing a novel forecasting model based on the idea to use the Pareto fronts as a tool to select data in the forecasting process. The proposed model was implemented to forecast short-term electric energy demand in Poland using historical hourly demand values from Polish TSO. The study rather intended on implementing the range of different approaches—scenarios of Pareto fronts usage than on a complex evaluation of the obtained results. However, performance of proposed models was compared with a few benchmark forecasting models, including naïve approach, SARIMAX, kNN, and regression. For two scenarios, it has outperformed all other models by minimum 7.7%.

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