Online Aggregation of Probabilistic Predictions of Hourly Electrical Loads

In this paper, we consider the problem of online probabilistic time series forecasting. The difference between a probabilistic prediction (distribution function) and a numerical outcome is measured using a loss function (scoring rule). In practical statistics, the Continuous Ranked Probability Score...

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Bibliographic Details
Published in:Journal of communications technology & electronics 2022-06, Vol.67 (6), p.702-716
Main Authors: V’yugin, V. V., Trunov, V. G.
Format: Article
Language:English
Subjects:
Agglomeration
Algorithms
Analysis
Communications Engineering
Confidence intervals
Distribution (Probability theory)
Distribution functions
Electrical loads
Electricity consumption
Engineering
Mathematical Models and Computational Methods
Networks
Predictions
Statistical analysis
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Summary:In this paper, we consider the problem of online probabilistic time series forecasting. The difference between a probabilistic prediction (distribution function) and a numerical outcome is measured using a loss function (scoring rule). In practical statistics, the Continuous Ranked Probability Score (CRPS) rule is often used to estimate the discrepancy between probabilistic predictions and quantitative outcomes. Here, we consider the case when several competing methods (experts) give their predictions in the form of distribution functions. Expert predictions are provided with confidence levels. We propose an algorithm for online aggregation of these distribution functions with allowance for the confidence levels to expert forecasts. The discounted error of the proposed algorithm with allowance for the confidence levels is estimated in the form of a comparison of the cumulative losses of the algorithm and the losses of experts. A technology for constructing predictive expert algorithms and aggregating their probabilistic predictions using the example of the problem of predicting electricity consumption 1 or more hours ahead was developed. The results of numerical experiments using real data are presented.
ISSN:1064-2269
1555-6557
DOI:10.1134/S1064226922060201