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Application of Deep Learning Method in Short-term Load Forecasting of Characteristic Enterprises

Published: 21 December 2018 Publication History

Abstract

Short-term load forecasting is an important basic work for the normal operation and control of power systems. The results of power load forecasting have a great impact on dispatching operation of the power system and the production operation of the enterprise. Accurate load forecasting would help improve the safety and stability of power system and save the cost of enterprise. In order to extract the effective information contained in the data and improve the accuracy of short-term load forecasting, this paper proposes a long-short term memory neural network model (LSTM) with deep learning ability for short-term load forecasting combined with clustering algorithm. Deep learning is in line with the trend of big data and has a strong ability to learn and summarize large amounts of data. Through the research on the characteristics and influencing factors of the characteristic enterprises, the collected samples are clustered to establish similar day sets. This paper also studies the impact of different types of load data on prediction and the actual problem of input training sample selection. The LSTM prediction model is built with subdividing and clustering the input load sample set. Compared with other traditional methods, the results prove that LSTM proposed has higher accuracy and applicability.

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  • (2021)Review of load data analytics using deep learning in smart grids: Open load datasets, methodologies, and application challengesInternational Journal of Energy Research10.1002/er.674545:10(14274-14305)Online publication date: 17-Apr-2021

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  1. Application of Deep Learning Method in Short-term Load Forecasting of Characteristic Enterprises

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    cover image ACM Other conferences
    AICCC '18: Proceedings of the 2018 Artificial Intelligence and Cloud Computing Conference
    December 2018
    206 pages
    ISBN:9781450366236
    DOI:10.1145/3299819
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    Published: 21 December 2018

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    Author Tags

    1. Clustering Algorithm
    2. Deep Learning
    3. Long-short Term Memory Neural Network Model
    4. Short-term Load Forecasting
    5. Similar Day Set

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    • (2021)Review of load data analytics using deep learning in smart grids: Open load datasets, methodologies, and application challengesInternational Journal of Energy Research10.1002/er.674545:10(14274-14305)Online publication date: 17-Apr-2021

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