Short-Term Load Forecasting for Smart Home Appliances with Sequence to Sequence Learning

Razghandi, Mina; Zhou, Hao; Erol‐Kantarci, Melike; Turgut, Damla

doi:10.1109/icc42927.2021.9500767

Cited by 16 publications

(4 citation statements)

References 15 publications

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“…For example, Qin et al 58 combined encoder–decoder with LSTM in DA‐RNN to forecast indoor temperature. Mina Razghandi and others 59 combined encoder–decoder and LSTM to establish a sequence‐to‐sequence forecasting model. Used to forecast the load power consumption of various household appliances.…”

Section: Methodsmentioning

confidence: 99%

A spatio‐temporal graph convolutional approach to real‐time load forecasting in an edge‐enabled distributed Internet of Smart Grids energy system

Liu,

Pan,

Cao

et al. 2024

Concurrency and Computation

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SummaryAs the edge nodes of the Internet of Smart Grids (IoSG), smart sockets enable all kinds of power load data to be analyzed at the edge, which create conditions for edge calculation and real‐time (RT) load forecasting. In this article, an edge‐cloud computing analysis energy system is proposed to collect and analyze power load data, and a combination of graph convolutional network (GCN) with LSTM, called KGLSTM is used to achieve mid‐long term mixed sequential mode RT forecasting. In the proposed edge‐cloud framework, distributed intelligent sockets are regarded as edge nodes to collect, analyze and upload data to cloud services for further processing. The proposed KGLSTM network adopts a double branch structure. One branch extracts the data characteristics of mid‐short term time‐series data through an encoding–decoding LSTM module; the other branch extracts the data features of long term timing data through an adapted GCN. GCN is used to extract spatial correlations between different nodes. In addition, by combining a dynamic weighted loss function, the accuracy of peak forecasting is effectively improved. Finally, through various experimental indicators, this article shows that KGLSTM and weighted KGLSTM have achieved significant performance improvement over recent methods in mid‐long term time‐series forecasting and peak forecasting.

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Section: Methodsmentioning

confidence: 99%

A spatio‐temporal graph convolutional approach to real‐time load forecasting in an edge‐enabled distributed Internet of Smart Grids energy system

Liu,

Pan,

Cao

et al. 2024

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

“…Each variable is affected not only by its own historical data but also by other variables. Commonly used analytical methods are Vector Autoregressive (VAR) (Jeong et al, 2021) and Vector Autoregressive Moving Average (VARMA) (Razghandi et al, 2021). The VAR model is a generalization of the univariate autoregressive model to a vector autoregressive model consisting of multivariate time series variables.…”

Section: Multivariate Time Series Forecastingmentioning

confidence: 99%

Review of multiple load forecasting method for integrated energy system

Liu,

Li,

et al. 2023

Front. Energy Res.

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In order to further improve the efficiency of energy utilization, Integrated Energy Systems (IES) connect various energy systems closer, which has become an important energy utilization mode in the process of energy transition. Because the complex and variable multiple load is an important part of the new power system, the load forecasting is of great significance for the planning, operation, control, and dispatching of the new power system. In order to timely track the latest research progress of the load forecasting method and grasp the current research hotspot and the direction of load forecasting, this paper reviews the relevant research content of the forecasting methods. Firstly, a brief overview of Integrated Energy Systems and load forecasting is provided. Secondly, traditional forecasting methods based on statistical analysis and intelligent forecasting methods based on machine learning are discussed in two directions to analyze the advantages, disadvantages, and applicability of different methods. Then, the results of Integrated Energy Systemss multiple load forecasting for the past 5 years are compiled and analyzed. Finally, the Integrated Energy Systems load forecasting is summarized and looked forward.

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“…The authors in [312] proposed a deep learning (DL)-based approach for predicting month-ahead hourly electrical demands by combining hourly electrical load and temperature data in SHs. An LSTM-based sequence-to-sequence learning model was developed by [313] for short-term load forecasting of SH appliances.…”

Section: Smart Homes 101 From Planning Perspectivesmentioning

confidence: 99%

Smart Distribution Mechanisms—Part I: From the Perspectives of Planning

et al. 2022

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To enhance the reliability and resilience of power systems and achieve reliable delivery of power to end users, smart distribution networks (SDNs) play a vital role. The conventional distribution network is transforming into an active one by incorporating a higher degree of automation. Replacing the traditional absence of manual actions, energy delivery is becoming increasingly dependent on intelligent active system management. As an emerging grid modernization concept, the smart grid addresses a wide range of economic and environmental concerns, especially by integrating a wide range of active technologies at distribution level. At the same time, these active technologies are causing a slew of technological problems in terms of power quality and stability. The development of such strategies and approaches that can improve SDN infrastructure in terms of planning, operation, and control has always been essential. As a result, a substantial number of studies have been conducted in these areas over the last 10–15 years. The current literature lacks a combined systematic analysis of the planning, operation, and control of SDN technologies. This paper conducts a systematic survey of the state-of-the-art advancements in SDN planning, operation, and control over the last 10 years. The reviewed literature is structured so that each SDN technology is discussed sequentially from the viewpoints of planning, operation, and then control. A comprehensive analysis of practical SND concepts across the globe is also presented in later sections. The key constraints and future research opportunities in the existing literature are discussed in the final part. This review specifically assists readers in comprehending current trends in SDN planning, operation, and control, as well as identifying the need for further research to contribute to the field.

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Short-Term Load Forecasting for Smart Home Appliances with Sequence to Sequence Learning

Cited by 16 publications

References 15 publications

A spatio‐temporal graph convolutional approach to real‐time load forecasting in an edge‐enabled distributed Internet of Smart Grids energy system

A spatio‐temporal graph convolutional approach to real‐time load forecasting in an edge‐enabled distributed Internet of Smart Grids energy system

Review of multiple load forecasting method for integrated energy system

Smart Distribution Mechanisms—Part I: From the Perspectives of Planning

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