Medium-term Load Forecast Based on Sequence Decomposition and Neural Network

Chen, Haowen; Liu, Wenxia; Yue-qiao, LI

doi:10.1109/cieec47146.2019.cieec-2019497

Cited by 8 publications

(2 citation statements)

References 9 publications

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“…The procedure for obtaining the forecast values is iterative. During each iteration, it is possible to obtain only one forecast value [27][28][29][30]. All forecast values are determined on the basis of the trajectory matrix reconstructed in a lower dimension.…”

Section: Waveletmentioning

confidence: 99%

Analysis of methods of electricity consumption forecasting for a coal industry enterprise

Manusov

Orlov

Karmanov

et al. 2022

IOP Conf. Ser.: Earth Environ. Sci.

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The paper considers a forecast model of electricity consumption of a coal industry enterprise based on three forecast methods, namely the wavelet transform, the vector method, and the recurrent neutral network. For preprocessing the data for forecasting by vector and recurrent methods, the Singular Spectrum Analysis method was chosen. The structure of the model allows taking into account individual features of the operating cycle of the production process and smoothing the noise components and outliers caused by these features. The results of a short-term hourly forecast for one day ahead are presented with the comparison of the obtained values. The results of short-term electricity consumption forecast were verified based on the actual data of the coal industry enterprise in order to assess the adequacy of the model to the actual values.

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Section: Waveletmentioning

confidence: 99%

Analysis of methods of electricity consumption forecasting for a coal industry enterprise

Manusov

Orlov

Karmanov

et al. 2022

IOP Conf. Ser.: Earth Environ. Sci.

View full text Add to dashboard Cite

show abstract

“…Furthermore, deep learning methods like convolutional neural networks (CNN) and recurrent neural networks (RNN) have found applications in load forecasting [3]. Moreover, load forecasting technology considers user behaviour patterns and weather conditions [4]. Use the different strengths of multiple models to improve the accuracy of the final predictive model [5].…”

Section: Introductionmentioning

confidence: 99%

Fed‐SAD: A secure aggregation federated learning method for distributed short‐term load forecasting

Li,

Wu,

Wang

et al. 2023

IET Generation Trans & Dist

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The distributed and privacy‐preserving attributes of fine‐grained smart grid data create obstacles to data sharing. As a result, federated learning emerges as an effective strategy for collaborative training in distributed load forecasting. However, poisoning attacks can interfere with training in the federated learning aggregation process, making it challenging to ensure the accuracy and safety of the global model in load forecasting. Therefore, the authors propose a secure aggregation federated learning method based on similarity and distance (Fed‐SAD) for distributed load forecasting. The server determines approximate global model parameters based on the similarity of the model parameters of the participants and aggregates the global model parameters using a distance‐based weighting method. Fed‐SAD effectively reduces the interference of poisoning attacks by securely aggregating models in short‐term load forecasting. Experimental results show that using the Fed‐SAD results in mean absolute percentage error (MAPE) reduction for certain participants by 19% on sign flipping attacks, MAPE reduction by 15% on additive noise attacks, and MAPE reduction by 4.5% without attack, compared to the Federated Average Aggregation algorithm. Furthermore, Fed‐SAD consistently maintains robustness and has excellent attack detection accuracy.

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Probabilistic forecasting method for mid-term hourly load time series based on an improved temporal fusion transformer model

Zhang

et al. 2023

International Journal of Electrical Power & Energy Systems

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Medium-term Load Forecast Based on Sequence Decomposition and Neural Network

Cited by 8 publications

References 9 publications

Analysis of methods of electricity consumption forecasting for a coal industry enterprise

Analysis of methods of electricity consumption forecasting for a coal industry enterprise

Fed‐SAD: A secure aggregation federated learning method for distributed short‐term load forecasting

Probabilistic forecasting method for mid-term hourly load time series based on an improved temporal fusion transformer model

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