Forecasting an electricity demand threshold to proactively trigger cost saving demand response actions

This paper presents a novel methodology that empowers virtually any electricity consumer paying for peak demand charges to proactively execute demand response actions even without receiving signals or information coming from the utility, and only when necessary to effectively reduce demand charges and user inconvenience. The proposed methodology employs different arithmetic models and tree-based machine learning models to determine an efficient electricity demand threshold value before the start of a billing period. This methodology is completely model agnostic so additional models can be integrated without changing the proposed process. The threshold value produced can be used to proactively trigger peak demand shaving and other demand response actions in order to reduce demand charges. The results obtained using real data showed that regression random decision forest based models outperformed different arithmetic models and other tree-based machine learning models at determining this threshold value for an industrial, an educational with solar photovoltaic electricity generation, and a residential consumer. The results also showed that the consumers evaluated could potentially achieve between 63% to 75% of total potential demand charge reductions during a year. These results translate to US$ 159.00, US$ 23,290.00, and US$ 107,389.00 in savings for the residential, industrial, and educational consumer respectively. (C) 2022 Elsevier B.V. All rights reserved.

Automated summary

This paper presents a novel methodology that allows electricity consumers to proactively reduce demand charges without relying on signals or information from the utility. The methodology employs different models to determine an efficient electricity demand threshold, which can be used to trigger demand response actions and achieve significant cost savings.