Deep Learning Based on Multi-Decomposition for Short-Term Load Forecasting

Kim, Seon Hyeog; Lee, Gyul; Kwon, Gu-Young; Kim, Do In; Shin, Yong-June

doi:10.3390/en11123433

Cited by 37 publications

(21 citation statements)

References 45 publications

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“…MAE is mean absolute error, which is a useful metric widely used in model evaluation [34]. MAPE is mean absolute percentage error between the predicted value and the truth value, which is able to avoid the offset problem of errors [35]. The definitions of the metrics are as follows:…”

Section: B Experiments Resultsmentioning

confidence: 99%

A Deep Learning Method for Short-Term Residential Load Forecasting in Smart Grid

et al. 2020

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Residential demand response is vital for the efficiency of power system. It has attracted much attention from both academic and industry in recent years. Accurate short-term load forecasting is a fundamental task for demand response. While short-term forecasting for aggregated load data has been extensively studied, load forecasting for individual residential users is still challenging due to the dynamic and stochastic characteristic of single users' electricity consumption behaviors, i.e., the variability of the residential activities. To address this challenge, this paper presents a short-term residential load forecasting framework, which makes use of the spatio-temporal correlation existing in appliances' load data through deep learning. Multiple time series are conducted in the framework to describe electricity consumption behaviors and their internal spatio-temporal relationship. And a method based on deep neural network and iterative ResBlock is proposed to learn the correlation among different electricity consumption behaviors for shortterm load forecasting. Experiments based on real world measurements have been conducted to evaluate the performance of the proposed forecasting approach. The results show that both the appliances' load data and iterative ResBlocks can help to improve the forecasting performance. Compared with existing methods, measurements on Root Mean Squared Error, Mean Absolute Error and Mean Absolute Percentage Error for the proposed approach are reduced by 3.89%-20.00%, 2.18%-22.58% and 0.69%-32.78%. In addition, further experiments are conducted to evaluate the impact of using appliances' load data, iterative ResBlocks as well as other factors for the proposed approach. INDEX TERMS Smart grid, short-term load forecasting, deep learning, residential load forecasting, iterative ResBlocks.

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Section: B Experiments Resultsmentioning

confidence: 99%

A Deep Learning Method for Short-Term Residential Load Forecasting in Smart Grid

et al. 2020

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“…MAPE represents the average value of the relative error between the predicted value and the actual value. It can avoid the problem that of errors being offset by each other [38]. Therefore, MAPE can accurately reflect the magnitude of the prediction error.…”

Section: Forecasting Performance Evaluationmentioning

confidence: 99%

Short-Term Electricity Load Forecasting Model Based on EMD-GRU with Feature Selection

et al. 2019

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Many factors affect short-term electric load, and the superposition of these factors leads to it being non-linear and non-stationary. Separating different load components from the original load series can help to improve the accuracy of prediction, but the direct modeling and predicting of the decomposed time series components will give rise to multiple random errors and increase the workload of prediction. This paper proposes a short-term electricity load forecasting model based on an empirical mode decomposition-gated recurrent unit (EMD-GRU) with feature selection (FS-EMD-GRU). First, the original load series is decomposed into several sub-series by EMD. Then, we analyze the correlation between the sub-series and the original load series through the Pearson correlation coefficient method. Some sub-series with high correlation with the original load series are selected as features and input into the GRU network together with the original load series to establish the prediction model. Three public data sets provided by the U.S. public utility and the load data from a region in northwestern China were used to evaluate the effectiveness of the proposed method. The experiment results showed that the average prediction accuracy of the proposed method on four data sets was 96.9%, 95.31%, 95.72%, and 97.17% respectively. Compared to a single GRU, support vector regression (SVR), random forest (RF) models and EMD-GRU, EMD-SVR, EMD-RF models, the prediction accuracy of the proposed method in this paper was higher.

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“…Variational modal decomposition (VMD) is a mature signal decomposition technology, which is also widely used in various fields [21]. Kim et al [22] proposed the VMD-LSTM model to study the load series and obtained satisfactory forecasting results. Moreover, this kind of hybrid model is also feasible in such research fields as wind speed forecasting, air quality forecast, and runoff prediction [23]- [25].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Forecasting Model for Short-Term Power Load Based on Sample Entropy, Two-Phase Decomposition and Whale Algorithm Optimized Support Vector Regression

Shi

Sibtain

et al. 2020

IEEE Access

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To improve the accuracy and reliability of short-term power load forecasting and reduce the difficulty caused by load volatility and non-linearity, a hybrid forecasting model (CEEMDAN-SE-VMD-PSR-WOA-SVR) is proposed. Firstly, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is employed to generate multiple intrinsic modal functions (IMF) by decomposing the historical power load series. Then the sample entropy (SE) of each IMF is calculated to quantitatively evaluate the corresponding complexity. Afterward, variational mode decomposition (VMD) is adopted to achieve secondary decomposition for the component with the maximum sample entropy. Subsequently, the phase space reconstruction (PSR) is applied to reconstruct each IMF into a high-dimensional feature space matrix, which is formed as the input of support vector regression (SVR). Finally, SVR optimized by whale optimization algorithm (WOA) is used for the prediction, where the predicted values of all IMFs are accumulated to obtain the final prediction results. The experimental result demonstrates that the proposed hybrid model can effectively decompose the load series with non-linear characteristic and provide more accurate forecasting results by comparing the other models. INDEX TERMS short-term power load forecasting, two-phase decomposition, sample entropy, whale optimization algorithm, support vector regression

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Deep Learning Based on Multi-Decomposition for Short-Term Load Forecasting

Cited by 37 publications

References 45 publications

A Deep Learning Method for Short-Term Residential Load Forecasting in Smart Grid

A Deep Learning Method for Short-Term Residential Load Forecasting in Smart Grid

Short-Term Electricity Load Forecasting Model Based on EMD-GRU with Feature Selection

A Hybrid Forecasting Model for Short-Term Power Load Based on Sample Entropy, Two-Phase Decomposition and Whale Algorithm Optimized Support Vector Regression

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