A Novel Short-Term Load Forecasting Method by Combining the Deep Learning With Singular Spectrum Analysis

Pham, Manh-Hai; Nguyen, Minh-Ngoc; Wu, Yuan‐Kang

doi:10.1109/access.2021.3078900

Cited by 27 publications

(11 citation statements)

References 29 publications

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“…Li et al [33] developed a combined forecasting model that uses LSTM and XGBoost methods to improve the accuracy of electric load forecasting. Pham et al [34] developed a hybrid forecasting method integrating singular spectrum analysis (SSA) and deep neural network techniques. Similarly, Ciechulski et al [35] used artificial neural networks and SSA techniques to investigate efficient and accurate methods for 1 h and 24 h power load pattern forecasting.…”

Section: Deep Learningmentioning

confidence: 99%

Research on Peak Load Prediction of Distribution Network Lines Based on Prophet-LSTM Model

Chen

Wang

et al. 2023

Sustainability

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The increasing demand for precise load forecasting for distribution networks has become a crucial requirement due to the continual surge in power consumption. Accurate forecasting of peak loads for distribution networks is paramount to ensure that power grids operate smoothly and to optimize their configuration. Many load forecasting methods do not meet the requirements for accurate data and trend fitting. To address these issues, this paper presents a novel forecasting model called Prophet-LSTM, which combines the strengths of the Prophet model’s high trend fitting and LSTM model’s high prediction accuracy, resulting in improved accuracy and effectiveness of peak load forecasting. The proposed algorithm models the distribution network peak load using the Prophet-LSTM algorithm. The researchers then analyzed the experimental data and model of the algorithm to evaluate its effectiveness. We found that the Prophet-LSTM algorithm outperformed the Prophet and LSTM models individually in peak load prediction. We evaluate the proposed model against commonly used forecasting models using MAE (mean absolute error) and RMSE (root mean square error) as evaluation metrics. The results indicate that the proposed model has better forecasting accuracy and stability. As a result, it can predict the peak load of distribution networks more accurately. In conclusion, this study offers a valuable contribution to load forecasting for distribution networks. The proposed Prophet-LSTM algorithm provides a more precise and stable prediction, making it a promising approach for future applications in distribution network load forecasting.

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Section: Deep Learningmentioning

confidence: 99%

Research on Peak Load Prediction of Distribution Network Lines Based on Prophet-LSTM Model

Chen

Wang

et al. 2023

Sustainability

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“…Given the strong periodicity of power system operation data, the SSA technique is chosen to process the operation data set and mine data information with different time resolutions. The matrix D formed by the power system operation data set decomposes the original time series into highfrequency, low-frequency, and noise matrices through normalization, matrix decomposition, and data reconstruction [20,21]. Different sampling point parameters, trend components, periodic components, and noise components with different time resolutions in each dimension of the power system operation data matrix D can be extracted to form a trend component matrix TD and a periodic component matrix SD.…”

Section: Decomposition Of Data Set Ssamentioning

confidence: 99%

Multi‐level refined power system operation mode analysis: A data‐driven approach

Bai

Meng

et al. 2022

IET Generation Trans & Dist

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The high renewable penetration will cause the power system operation mode (PSOM) to change frequently. At present, the selection of PSOM mainly depends on the experience of relevant staff. However, selecting PSOM based on experience is difficult to comprehensively and elaborately analyze the possible PSOMs within a dispatching cycle, which inevitably invites troubles to the power system planning and dispatching. In this paper, a data‐driven analysis method is proposed, which integrates the techniques of preprocessing, dimensionality reduction, clustering, and data visualization of high‐dimensional data sets of power system operation. Simultaneously, qualitative and quantitative methods are used to analyze, evaluate the PSOM and their development laws. Finally, to verify the effectiveness of the proposed multi‐level refinement extraction method of PSOM, the normal power system operation mode (NPSOM), transition power system operation mode (TPSOM), extreme power system operation mode (EPSOM) are obtained and analyzed according to the real operation data of the urban power system. The abundant analysis results show that the proposed fine‐grained extraction method of PSOM is compelling, facilitating the power grid enterprise operation and planning.

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“…According to the above analysis, some key elements are missing from the current studies on electricity price forecasting, namely: (a) Many current studies ignore the useless information brought by the large amount of electricity price data when screening data features, which not only causes a reduction in forecasting accuracy but also affects the operational efficiency of forecasting models [34]. (b) The existing prediction models are generally based on a single sample set composed of features, which leads to the extraction of too much data, resulting in poor prediction accuracy [35,36].…”

Section: Introductionmentioning

confidence: 99%

Day-Ahead Spot Market Price Forecast Based on a Hybrid Extreme Learning Machine Technique: A Case Study in China

et al. 2022

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With the deepening of China’s electricity spot market construction, spot market price prediction is the basis for making reasonable quotation strategies. This paper proposes a day-ahead spot market price forecast based on a hybrid extreme learning machine technology. Firstly, the trading center’s information is examined using the Spearman correlation coefficient to eliminate characteristics that have a weak link with the price of power. Secondly, a similar day-screening model with weighted grey correlation degree is constructed based on the grey correlation theory (GRA) to exclude superfluous samples. Thirdly, the regularized limit learning machine (RELM) is tuned using the Marine Predators Algorithm (MPA) to increase RELM parameter accuracy. Finally, the proposed forecasting model is applied to the Shanxi spot market, and other forecasting models and error computation methodologies are compared. The results demonstrate that the model suggested in this paper has a specific forecasting effect for power price forecasting technology.

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A Novel Short-Term Load Forecasting Method by Combining the Deep Learning With Singular Spectrum Analysis

Cited by 27 publications

References 29 publications

Research on Peak Load Prediction of Distribution Network Lines Based on Prophet-LSTM Model

Research on Peak Load Prediction of Distribution Network Lines Based on Prophet-LSTM Model

Multi‐level refined power system operation mode analysis: A data‐driven approach

Day-Ahead Spot Market Price Forecast Based on a Hybrid Extreme Learning Machine Technique: A Case Study in China

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