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      Review of Family-Level Short-Term Load Forecasting and Its Application in Household Energy Management System

      , , , ,
      Energies
      MDPI AG

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          Abstract

          With the rapid development of smart grids and distributed energy sources, the home energy management system (HEMS) is becoming a hot topic of research as a hub for connecting customers and utilities for energy visualization. Accurate forecasting of future short-term residential electricity demand for each major appliance is a key part of the energy management system. This paper aims to explore the current research status of household-level short-term load forecasting, summarize the advantages and disadvantages of various forecasting methods, and provide research ideas for short-term household load forecasting and household energy management. Firstly, the paper analyzes the latest research results and research trends in deep learning load forecasting methods in terms of network models, feature extraction, and adaptive learning; secondly, it points out the importance of combining probabilistic forecasting methods that take into account load uncertainty with deep learning techniques; and further explores the implications and methods for device-level as well as ultra-short-term load forecasting. In addition, the paper also analyzes the importance of short-term household load forecasting for the scheduling of electricity consumption in household energy management systems. Finally, the paper points out the problems in the current research and proposes suggestions for future development of short-term household load forecasting.

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          Long Short-Term Memory

          Learning to store information over extended time intervals by recurrent backpropagation takes a very long time, mostly because of insufficient, decaying error backflow. We briefly review Hochreiter's (1991) analysis of this problem, then address it by introducing a novel, efficient, gradient-based method called long short-term memory (LSTM). Truncating the gradient where this does not do harm, LSTM can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units. Multiplicative gate units learn to open and close access to the constant error flow. LSTM is local in space and time; its computational complexity per time step and weight is O(1). Our experiments with artificial data involve local, distributed, real-valued, and noisy pattern representations. In comparisons with real-time recurrent learning, back propagation through time, recurrent cascade correlation, Elman nets, and neural sequence chunking, LSTM leads to many more successful runs, and learns much faster. LSTM also solves complex, artificial long-time-lag tasks that have never been solved by previous recurrent network algorithms.
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            • Record: found
            • Abstract: not found
            • Article: not found

            Probabilistic electric load forecasting: A tutorial review

              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found

              Smart home energy management systems: Concept, configurations, and scheduling strategies

                Bookmark

                Author and article information

                Contributors
                Journal
                ENERGA
                Energies
                Energies
                MDPI AG
                1996-1073
                August 2023
                August 04 2023
                : 16
                : 15
                : 5809
                Article
                10.3390/en16155809
                534bf9a8-c3b8-4c8f-9578-a232c9270528
                © 2023

                https://creativecommons.org/licenses/by/4.0/

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