GM (1,1) model application in power load forecasting based on moving 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.

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A new intelligent method for power load forecasting

2006 China International Conference on Electricity Distribution (CICED 2006) ◽

10.1049/cp:20061674 ◽

2006 ◽

Cited By ~ 1

Author(s):

Wei Li ◽

Renhui Huang ◽

Jie Bai ◽

Dongxiao Niu

Keyword(s):

Load Forecasting ◽

Power Load ◽

Power Load Forecasting

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Forecasting for Ultra-Short-Term Electric Power Load Based on Integrated Artificial Neural Networks

Symmetry ◽

10.3390/sym11081063 ◽

2019 ◽

Vol 11 (8) ◽

pp. 1063 ◽

Cited By ~ 2

Author(s):

Horng-Lin Shieh ◽

Fu-Hsien Chen

Keyword(s):

Energy Efficiency ◽

Electric Power ◽

Production Efficiency ◽

Load Forecasting ◽

Short Term ◽

Inference System ◽

Power Load ◽

Power Load Forecasting ◽

New Energy ◽

Electric Power Load

Energy efficiency and renewable energy are the two main research topics for sustainable energy. In the past ten years, countries around the world have invested a lot of manpower into new energy research. However, in addition to new energy development, energy efficiency technologies need to be emphasized to promote production efficiency and reduce environmental pollution. In order to improve power production efficiency, an integrated solution regarding the issue of electric power load forecasting was proposed in this study. The solution proposed was to, in combination with persistence and search algorithms, establish a new integrated ultra-short-term electric power load forecasting method based on the adaptive-network-based fuzzy inference system (ANFIS) and back-propagation neural network (BPN), which can be applied in forecasting electric power load in Taiwan. The research methodology used in this paper was mainly to acquire and process the all-day electric power load data of Taiwan Power and execute preliminary forecasting values of the electric power load by applying ANFIS, BPN and persistence. The preliminary forecasting values of the electric power load obtained therefrom were called suboptimal solutions and finally the optimal weighted value was determined by applying a search algorithm through integrating the above three methods by weighting. In this paper, the optimal electric power load value was forecasted based on the weighted value obtained therefrom. It was proven through experimental results that the solution proposed in this paper can be used to accurately forecast electric power load, with a minimal error.

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