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 5 publications

(1 citation statement)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%