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Adaptive Feature Selection for Probabilistic Multi-Energy Load Forecasting
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AI
DOI：10.1109/TIA.2023.3344540.png)
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Abstract
En 中文
Accurate probabilistic forecasting of the multi-energy loads can provide essential uncertainty information about future loads for the management of integrated energy systems. The selection of appropriate features lays a critical foundation to achieve accurate forecasting, but such an issue is not thoroughly studied for probabilistic load forecasting, especially for multi-energy loads. In this article, we propose an adaptive feature selection framework for probabilistic multi-energy load forecasting by considering different operation patterns to select pattern-specific features. Specifically, we develop a ProbLassoNet model by integrating the multi-quantile regression model with the residual-connecting-Lasso operation to capture both linearity and nonlinearity for effective feature selection. We conduct experiments on an open dataset and validate that the proposed method can significantly improve probabilistic multi-energy load forecasting by distinguishing important features from redundant features. We also provide a comprehensive analysis of important features and multi-energy relationships in different periods, which can serve as a reference for further research on multi-energy load forecasting.
Keywords:
Forecasting
Load forecasting
Probabilistic logic
Load modeling
Feature extraction
Predictive models
Cooling
Multi-energy system
probabilistic load forecasting
feature selection
Journal
IF:
4.5
Papers:
1.1W
Citations:
3.5W