A genetic-algorithm-based remnant grey prediction model for energy demand forecasting

Energy demand is an important economic index, and demand forecasting has played a significant role in drawing up energy development plans for cities or countries. As the use of large datasets and statistical assumptions is often impractical to forecast energy demand, the GM(1,1) model is commonly us...

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Bibliographic Details
Published in:PloS one 2017-10, Vol.12 (10), p.e0185478-e0185478
Main Author: Hu, Yi-Chung
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Analysis
Artificial intelligence
Biology and Life Sciences
China
Computer and Information Sciences
Data points
Demand
Design optimization
Economic aspects
Economic forecasting
Economic models
Energy consumption
Energy demand
Energy industries
Forecasting
Forecasts and trends
Genetic algorithms
Grey prediction
Integrated circuits
Mathematical models
Methods
Models, Theoretical
Physical Sciences
Power
Research and Analysis Methods
Social change
Supply-demand forecasting
Time series
Tourism
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Summary:Energy demand is an important economic index, and demand forecasting has played a significant role in drawing up energy development plans for cities or countries. As the use of large datasets and statistical assumptions is often impractical to forecast energy demand, the GM(1,1) model is commonly used because of its simplicity and ability to characterize an unknown system by using a limited number of data points to construct a time series model. This paper proposes a genetic-algorithm-based remnant GM(1,1) (GARGM(1,1)) with sign estimation to further improve the forecasting accuracy of the original GM(1,1) model. The distinctive feature of GARGM(1,1) is that it simultaneously optimizes the parameter specifications of the original and its residual models by using the GA. The results of experiments pertaining to a real case of energy demand in China showed that the proposed GARGM(1,1) outperforms other remnant GM(1,1) variants.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0185478