Abstract
In order to improve the accuracy of forecasts of the electricity sales of power sales companies, a depth forecast model of electricity sales based on the characteristics of the power market is proposed. First, based on survival analysis, the calculation method of the user churn rate in the electricity market is given, and the number of users at a certain moment in the future is predicted. Then, users’ electricity consumption that calculated by the deep belief network and the predicted quantity of users are combined to design a forecast model of electricity sales. Finally, the model is solved utilizing the weighting algorithm of adaptive inertia. The analysis of the example shows that the proposed method achieves a significant improvement in the accuracy of power sales forecasting.
| Original language | English |
|---|---|
| Pages (from-to) | 1585-1596 |
| Number of pages | 12 |
| Journal | Journal of Electrical Engineering and Technology |
| Volume | 14 |
| Issue number | 4 |
| Early online date | 29 May 2019 |
| DOIs | |
| Publication status | Published - 1 Jul 2019 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 7 Affordable and Clean Energy
Keywords
- Churn rate of users
- Deep belief network
- Electricity market
- Electricity sales
ASJC Scopus subject areas
- Electrical and Electronic Engineering
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