Probabilistic Load Forecasting Based on Adaptive Online Learning

TL;DR

This paper introduces a probabilistic load forecasting method using adaptive online learning of hidden Markov models, effectively capturing uncertainties and adapting to dynamic consumption patterns in various energy scenarios.

Contribution

It presents a novel adaptive online learning approach for hidden Markov models that improves probabilistic load forecasting accuracy and adaptability over existing methods.

Findings

Significantly improves forecasting performance across multiple datasets.

Effectively captures uncertainties in load demand.

Adapts to changing consumption patterns in real-time.

Abstract

Load forecasting is crucial for multiple energy management tasks such as scheduling generation capacity, planning supply and demand, and minimizing energy trade costs. Such relevance has increased even more in recent years due to the integration of renewable energies, electric cars, and microgrids. Conventional load forecasting techniques obtain single-value load forecasts by exploiting consumption patterns of past load demand. However, such techniques cannot assess intrinsic uncertainties in load demand, and cannot capture dynamic changes in consumption patterns. To address these problems, this paper presents a method for probabilistic load forecasting based on the adaptive online learning of hidden Markov models. We propose learning and forecasting techniques with theoretical guarantees, and experimentally assess their performance in multiple scenarios. In particular, we develop adaptive online learning techniques that update model parameters recursively, and sequential prediction techniques that obtain probabilistic forecasts using the most recent parameters. The performance of the method is evaluated using multiple datasets corresponding with regions that have different sizes and display assorted time-varying consumption patterns. The results show that the proposed method can significantly improve the performance of existing techniques for a wide range of scenarios.