Short-term Load Forecasting of Central China based on DPSO-LSTM

Yang, Jianhua; Zhang, Xinze; Bao, Yukun

doi:10.1109/cieec50170.2021.9510278

Cited by 3 publications

(1 citation statement)

References 10 publications

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“…Lv et al [ 11 ] designed a LightGBM-optimized LSTM to realize short-term stock prices. To improve the demand prediction accuracy in the case of single sample data, Mei et al [ 12 ] proposed a model based on multiscale temporal features for LSTM. First, wavelet decomposition decomposes historical data into stable components, trend demand, and periodic series such as the response peak-valley magnitude and duration, highlighting different time-scale features.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Demand Forecasting Method in Power Markets Based on the KSVM–TCN–GBRT

Yang

Duan

et al. 2022

Computational Intelligence and Neuroscience

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With the consumption of new energy and the variability of user activity, accurate and fast demand forecasting plays a crucial role in modern power markets. This paper considers the correlation between temperature, wind speed, and real-time electricity demand and proposes a novel method for forecasting short-term demand in the power market. Kernel Support Vector Machine is first used to classify real-time demand in combination with temperature and wind speed, and then the temporal convolutional network (TCN) is used to extract the temporal relationships and implied information of day-ahead demand. Finally, the Gradient Boosting Regression Tree is used to forecast daily and weekly real-time demand based on electrical, meteorological, and data characteristics. The validity of the method was verified using a dataset from the ISO-NE (New England Electricity Market). Comparative experiments with existing methods showed that the method could provide more accurate demand forecasting results.

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Section: Introductionmentioning

confidence: 99%

Short-Term Demand Forecasting Method in Power Markets Based on the KSVM–TCN–GBRT

Yang

Duan

et al. 2022

Computational Intelligence and Neuroscience

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PredXGBR: A Machine Learning Based Short-Term Electrical Load Forecasting Architecture

Zabin

Barua

Ahmed

2023

Proceedings of International Conference on Information and Communication Technology for Development

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Study on Short-Term Electricity Load Forecasting Based on the Modified Simplex Approach Sparrow Search Algorithm Mixed with a Bidirectional Long- and Short-Term Memory Network

Zhang,

Gou

et al. 2024

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In order to balance power supply and demand, which is crucial for the safe and effective functioning of power systems, short-term power load forecasting is a crucial component of power system planning and operation. This paper aims to address the issue of low prediction accuracy resulting from power load volatility and nonlinearity. It suggests optimizing the number of hidden layer nodes, number of iterations, and learning rate of bi-directional long- and short-term memory networks using the improved sparrow search algorithm, and predicting the actual load data using the load prediction model. Using actual power load data from Wuxi, Jiangsu Province, China, as a dataset, the model makes predictions. The results indicate that the model is effective because the enhanced sparrow algorithm optimizes the bi-directional long- and short-term memory network model for predicting the power load data with a relative error of only 2%, which is higher than the prediction accuracy of the other models proposed in the paper.

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Short-term Load Forecasting of Central China based on DPSO-LSTM

Cited by 3 publications

References 10 publications

Short-Term Demand Forecasting Method in Power Markets Based on the KSVM–TCN–GBRT

Short-Term Demand Forecasting Method in Power Markets Based on the KSVM–TCN–GBRT

PredXGBR: A Machine Learning Based Short-Term Electrical Load Forecasting Architecture

Study on Short-Term Electricity Load Forecasting Based on the Modified Simplex Approach Sparrow Search Algorithm Mixed with a Bidirectional Long- and Short-Term Memory Network

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