Abstract
Accurate short-term load forecasting is one of the key technologies to ensure the safe and stable operation of new power systems. However, residential load forecasting faces the difficulties of huge number of users, high load heterogeneity, high volatility and high randomness. With the increase of user types and data, the complexity of the model will increase significantly, making it difficult to meet the requirements of load forecasting in new power systems. Therefore, this paper develops a structured long- and short-term neural network model based on prediction-oriented autoencoders, which can accurately forecast the short-term load of all types of users through a single model. Compared with similar models, the prediction accuracy of the 13 combined models proposed in this paper is improved by 7.16%∼11.59%, and it is also of great referential significance for the unified prediction of complex high-frequency time series of highly heterogeneous subjects in non-electricity fields.
| Translated title of the contribution | Global Short-Term Load Forecasting for Multi Decision Making Units in the New Power System |
|---|---|
| Original language | Chinese (Traditional) |
| Pages (from-to) | 106-125 |
| Number of pages | 20 |
| Journal | China Journal of Econometrics |
| Volume | 2 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - Jan 2022 |