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https://doi.org/10.17559/TV-20230708000790

Power Load Forecasting Based on LSTM Deep Learning Algorithm

Dalei Wu ; Electric Energy Metering Center, Hainan Power Grid Co., Ltd., Hainan, China *
Shuhua Liang ; Electric Energy Metering Center, Hainan Power Grid Co., Ltd., Hainan, China
Changji Chen ; Wuzhishan Power Supply Bureau of Hainan, Power Grid Co., Ltd., Hainan, China
Yupei Chen ; Electric Energy Metering Center, Hainan Power Grid Co., Ltd., Hainan, China
Pishi Wang ; Electric Energy Metering Center, Hainan Power Grid Co., Ltd., Hainan, China
Zhiyuan Long ; Electric Energy Metering Center, Hainan Power Grid Co., Ltd., Hainan, China

* Dopisni autor.

Sažetak

In recent years, the scale of China's power grid has been expanding, and the electricity consumption load has been rising year by year. Load forecasting plays a crucial role in ensuring the efficient coordination of power generation, transmission, and distribution in intelligent power systems. It holds immense significance in the planning, operation, control, and scheduling of new power systems. However, many current forecasting models do not take into account the temporal relationship of electricity consumption data, and therefore the models do not perform very well. In order to improve the accuracy of electricity consumption prediction, a Long Short Term Memory neural network model is proposed. We collect a series of electricity consumption data based on a fixed time interval, and use the daily collected data as a time series, and use Long Short Term Memory to build a simulated ensemble for the time series. Considering the impact of different acquisition intervals on the prediction results, we conducted experiments on load prediction with different sampling intervals. Our experiments were all performed on the data provided by the test questions of the 9th Electrician Mathematical Modeling Contest 2016.

Ključne riječi

electricity consumption forecast; long Short Term Memory; time series data

Hrčak ID:

321954

URI

https://hrcak.srce.hr/321954

Datum izdavanja:

31.10.2024.

Posjeta: 130 *