Abstract
With the complete implementation of the “Replacement of Coal with Electricity” policy, electric loads borne by urban power systems have achieved explosive growth. The traditional load forecasting method based on “similar days” only applies to the power systems with stable load levels and fails to show adequate accuracy. Therefore, a novel load forecasting approach based on long short-term memory (LSTM) was proposed in this paper. The structure of LSTM and the procedure are introduced firstly. The following factors have been fully considered in this model: time-series characteristics of electric loads; weather, temperature, and wind force. In addition, an experimental verification was performed for “Replacement of Coal with Electricity” data. The accuracy of load forecasting was elevated from 83.2 to 95%. The results indicate that the model promptly and accurately reveals the load capacity of grid power systems in the real application, which has proved instrumental to early warning and emergency management of power system faults.
Highlights
Due to the impact of air pollution and energy shortages, Beijing, China has introduced a “Replacement of Coal with Electricity” policy that encourages household users to use electricity for heating instead of traditional coal-fired heatingThese authors contributed to this work: Zexi Chen and Delong Zhang.[1]
The major technology adopted in the policy of “Replacement of Coal with Electricity” is the air source heat pump [3], which has been utilized for heating in winter but appreciably increases the electric load at the same time
(1) A load forecasting method based on the long short-term memory (LSTM) model is proposed, which take many factors, such as temperature, wind force, into account and avoids the shortages of gradient disappearance or explosion
Summary
Due to the impact of air pollution and energy shortages, Beijing, China has introduced a “Replacement of Coal with Electricity” policy that encourages household users to use electricity for heating instead of traditional coal-fired heating. A load forecasting model based on LSTM is established first, and the structure of its hidden layer neural unit is introduced. The model fully considers the time series characteristics of the power load Based on this model, the problem of forgetting long-term training data of traditional NN and FNN can be avoided. (1) A load forecasting method based on the LSTM model is proposed, which take many factors, such as temperature, wind force, into account and avoids the shortages of gradient disappearance or explosion. This model can reflect the load capacity of the power grid in a timely and accurate manner.
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