Medium-long Term Load Forecasting of Power System Based on Interval Taylor Model Algorithm

Zheng, Zhong; Wang, Fei; Zhao, Long; Fu, Yimu

doi:10.1109/ei250167.2020.9347374

Cited by 2 publications

(1 citation statement)

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“…A large amount of literature explored different methods to overcome this problem. For example, the literature [7] adopted Inter Taylor Model Algorithm (ITMA) to construct a new medium-long term forecasting model, and aimed to "avoid error criterion pf the relevance between variables and reducing over estimation issue" (P1604). Other research used ANN [8], improved grey theory model [9][10], and fuzzy theory [11] to elevate the prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

A novel medium- and long-term load forecasting method based on sensitivity analysis of multi meteorological indicators

Sun¹,

Yang²,

Wang³

et al. 2023

Eighth International Symposium on Advances in Electrical, Electronics, and Computer Engineering (ISAEECE 2023)

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Traditional power load or electricity forecasting methods have used only limited meteorological information. Specifically, these methods either only select one of maximal, minimal and average temperature as the influencing indicator, or only capture the very basic meteorological information, instead of exploring the nonlinear relationship between temperature and loads in depth. To overcome the shortcomings of the traditional methods, this paper proposes a new medium- and long-term load forecasting method based on sensitivity analysis of multi meteorological indicators. This method constructs the encoder and decoder between weather features and loads. Moreover, this paper combines this method with XGBoost and SVR, which can improve the accuracy of medium- and long-term forecasting algorithm in the power grid field effectively. The simulation results of this synthesized model show that average daily prediction accuracy rate of the year is 95.37% and the average monthly prediction accuracy rate of the year is 98.02%.

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