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A noval space-time feature extraction approach for load forecasting | IEEE Conference Publication | IEEE Xplore

A noval space-time feature extraction approach for load forecasting


Abstract:

Precisely load forecasting is one of the most important points in power system researches. Especially in distribution network, an accurate load forecasting can significan...Show More

Abstract:

Precisely load forecasting is one of the most important points in power system researches. Especially in distribution network, an accurate load forecasting can significantly facilitate the power system control. Therefore this paper presents a novel load forecasting approach combining the features of the load time-sequenced with spatial distribution characteristics, which enables the analysis of the dynamic behaviors of the power system operation. Because of the outlier tolerance benefitting from the space-time domain analysis, the presented approach is able to supply reliable and efficient predicted load forecasting results. In order to evaluate the performances of the approach, a practical dataset of a real distribution system from Northeast China has been employed. The experimental results show that the precision of the load forecasting can be effectively improved by the proposed method, which outperforms weighted daily periodicity model algorithms in prediction accuracy and robustness.
Date of Conference: 29-31 July 2017
Date Added to IEEE Xplore: 25 June 2018
ISBN Information:
Conference Location: Guilin, China

I. Introduction

Load forecasting in power system is one of the most important key of helping the system control as well as grid planning. Especially in distribution networks, an accurate load forecasting can greatly facilitate the system control, which contributes a lot of the system stability. The accurate load forecasting also helps the power system to be aware of the flow distribution across the grid, for example, varying of the load, valley of the load, and peak of the load. However, in the current power grid, two critical factors introduce significant uncertainty and randomness of load forecasting. The first is the rapid growth of the system scale, whilst the second is the deployment of the new energy including wind power and photovoltaic power. Therefore, an accurate load forecasting approach which can reduce the errors caused by the uncertainty and randomness is anxiously needed for serving the modern power system.

References

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