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Short-Term Load Forecasting for Regional Power Grids Based on Correlation Analysis and Feature Extraction | IEEE Conference Publication | IEEE Xplore

Short-Term Load Forecasting for Regional Power Grids Based on Correlation Analysis and Feature Extraction


Abstract:

Currently, a high proportion of new energy is connected to the distributed power grid, mainly wind power and photovoltaic, which increases the uncertainty of the forecast...Show More

Abstract:

Currently, a high proportion of new energy is connected to the distributed power grid, mainly wind power and photovoltaic, which increases the uncertainty of the forecasting environment. For this specific environment, this paper proposes a short-term load forecasting (STLF) method for regional power grids to meet the demand of enterprises for forecasting accuracy. The method improves forecasting accuracy through three stages: data correlation analysis, feature extraction, and load forecasting. More specifically, in the correlation analysis stage, this paper uses the Pearson correlation coefficient (PCC) to explore the correlation between meteorological factors and electric load. The strongly correlated meteorological variables are selected based on the value of PCC. In the feature extraction stage, this paper uses a convolutional neural network (CNN) as a feature extractor to extract more representative feature information from the input data. In the load forecasting stage, a long-short term memory (LSTM) model is established. It has a unique gate structure to limit the impact of the input data on the LSTM's parameters, thus improving the model's prediction performance. Finally, we demonstrate that the proposal has high STLF accuracy.
Date of Conference: 15-17 April 2022
Date Added to IEEE Xplore: 01 June 2022
ISBN Information:
Conference Location: Hangzhou, China

I. Introduction

Accurate electrical load forecasting has essential practical value and significance to ensure power systems' economic and safe operation [1]. With the development of communication and measurement technologies, the power sector has obtained a huge amount of information data, which provides big data support to improve the prediction model's performance.

References

References is not available for this document.