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Short-Term Electrical Load Forecasting With Multidimensional Feature Extraction | IEEE Journals & Magazine | IEEE Xplore

Short-Term Electrical Load Forecasting With Multidimensional Feature Extraction


Abstract:

Accurate short-term load forecasting (STLF) is required for reliable power system operations. Nevertheless, load forecasting remains a challenge owing to the high dimensi...Show More

Abstract:

Accurate short-term load forecasting (STLF) is required for reliable power system operations. Nevertheless, load forecasting remains a challenge owing to the high dimensionality and volatility of electrical load data as time series. In this study, a feature extraction framework for electrical load and other complementary data as a multivariate time series is proposed. The proposed framework consists of tagging and embedding processes that extract patterns from the multivariate time series as tags and capture their temporal and dimensional relations. In the embedding process, a network model that embeds the tags is deliberately designed with a convolutional layer in a multi-output structure based on mathematical analysis. Furthermore, a deep learning-based STLF model is constructed with the proposed feature extraction framework. The performance of the proposed STLF model for day-ahead load forecasting is evaluated on a publicly available set of real electricity demand data. The experimental results verify that the proposed approach reduces the root mean squared error by 5% to 12%. This improvement in load forecasts can benefit power grid operations as it provides more accurate expectations on the behaviors of the power grid in short term, which can be utilized in power grid applications, such as power dispatch and scheduling.
Published in: IEEE Transactions on Smart Grid ( Volume: 13, Issue: 4, July 2022)
Page(s): 2999 - 3013
Date of Publication: 10 March 2022

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I. Introduction

Electrical load forecasting has played a key role in power system operation and management as the basis of various power system analyses [1]. In particular, short-term load forecasting (STLF), which commonly implies from hour-ahead to week-ahead forecasts, is used in many power grid applications to ensure reliable operations of power grids [2], [3], [4]. For instance, day-ahead demand forecasts are utilized in load smoothing and peak shaving [5]. The sub-hourly electrical load forecasts are also considered as the key information required to optimize energy storage systems for frequency response [6], [7]. In addition, economic dispatch optimization and scheduling are investigated along with electrical load forecasting for multi-microgrids and decentralized co-generation plants [8], [9]. Furthermore, day-ahead grid load forecast is used as the input feature for the prediction on the next-day market price of electricity [10]. Accordingly, accurate STLF facilitates reliable and efficient power system operations through supporting power grid applications. Nevertheless, STLF remains a challenge owing to the high dimensionality and volatility of the electrical load data as a time series.

References

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