A VVWBO-BVO-based GM (1,1) and its parameter optimization by GRA-IGSA integration algorithm for annual power load forecasting.

Lianhui Li,Hongguang Wang,Yong Deng

doi:10.1371/journal.pone.0196816

Lianhui Li, Hongguang Wang + Show 1 more

Open Access

https://doi.org/10.1371/journal.pone.0196816

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Abstract

Annual power load forecasting is not only the premise of formulating reasonable macro power planning, but also an important guarantee for the safety and economic operation of power system. In view of the characteristics of annual power load forecasting, the grey model of GM (1,1) are widely applied. Introducing buffer operator into GM (1,1) to pre-process the historical annual power load data is an approach to improve the forecasting accuracy. To solve the problem of nonadjustable action intensity of traditional weakening buffer operator, variable-weight weakening buffer operator (VWWBO) and background value optimization (BVO) are used to dynamically pre-process the historical annual power load data and a VWWBO-BVO-based GM (1,1) is proposed. To find the optimal value of variable-weight buffer coefficient and background value weight generating coefficient of the proposed model, grey relational analysis (GRA) and improved gravitational search algorithm (IGSA) are integrated and a GRA-IGSA integration algorithm is constructed aiming to maximize the grey relativity between simulating value sequence and actual value sequence. By the adjustable action intensity of buffer operator, the proposed model optimized by GRA-IGSA integration algorithm can obtain a better forecasting accuracy which is demonstrated by the case studies and can provide an optimized solution for annual power load forecasting.

Highlights

MotivationAccurate power load forecasting is the key to realize the sustainable development of the power industry and ensure the reliable and safe operation of the power grid
The action intensity of the traditional buffer operator is nonadjustable and the fixed buffer operator is less adaptable. This will lead to an uncertain buffer effect, so we introduce variable-weight weakening buffer operator (VWWBO) into the traditional GM (1,1) forecasting model for the dynamic pre-process of historical annual power data
The above three models are applied for annual power load forecasting, and percentage error (PE), mean absolute percentage error (MAPE) and grey relativity between simulating value and actual value are used as the indicators to evaluate the above three models

Summary

Introduction

Accurate power load forecasting is the key to realize the sustainable development of the power industry and ensure the reliable and safe operation of the power grid. In the past few years, many scholars have given a lot of attention to short-term power load forecasting based on different factors and have reached a higher level. Annual power load forecasting can provide reliable guidance for power grid operation and power construction planning [1,2,3,4]. Economic, climate and other factors, an annual load curve often shows a non-linear characteristic and has a certain degree of mutation. The difficulty of annual power load forecasting is obviously larger than short-term power load prediction

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Results

Conclusion