PeakTK: An Open Source Toolkit for Peak Forecasting in Energy Systems

Bovornkeeratiroj, Phuthipong; Wamburu, John; Irwin, David; Shenoy, Prashant

doi:10.1145/3530190.3534791

Cited by 3 publications

References 25 publications

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Online demand peak shaving with machine‐learned advice in digital twins

Feng,

Li,

Qin

et al. 2024

Digital Twins and Applications

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As the use of physical instruments grows, control algorithms are being increasingly deployed to enhance efficiency and reliability through digital twin technology. Demand load management is central to energy systems within digital twins, which significantly impacts operational costs. Peak demand loads can lead to substantial monthly utility expenses without proper management. AMPAMOD, a randomised online algorithm incorporating machine‐learned insights is introduced to optimise battery operations and mitigate peak demand loads. AMPAMOD leverages limited‐bit information from machine learning models to inform its online decision‐making process for cost‐effective load management. We provide theoretical evidence demonstrating that AMPAMOD maintains minimal advice complexity, has a linear computational cost, and achieves a bounded competitive ratio. Extensive trace‐driven experiments with real‐world household data reveal that AMPAMOD successfully reduces peak loads by over 90%, outperforming other benchmarks by at least 50%. These experimental findings align with our theoretical assertions, showcasing the effectiveness of AMPAMOD.

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Online demand peak shaving with machine‐learned advice in digital twins

Feng,

Li,

Qin

et al. 2024

Digital Twins and Applications

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Predicting the magnitude and timing of peak electricity demand: A competition case study

Donaldson,

Browell,

Gilbert

2023

IET Smart Grid

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As weather dependence of the electricity network grows, there is an increasing need to predict the time at which the network peak load will occur. Improving forecasts of peak hour can lead to more accurate scheduling of generation as well as the ability to use flexibility to improve system utilisation or defer capital investment. While there are extensive benchmark models for forecasting electricity demand, their efficacy at forecasting the time or shape of the peak remains to be seen. Global forecasting competitions provide a unique opportunity to compare multiple methodologies under common performance criteria and incentives. The methodology and results are detailed from the Big Data and Energy Analytics Laboratory Challenge 2022 used by the team ‘peaky‐finders’ and investigates the suitability of using hourly methods to forecast daily peak magnitude, time, and shape. The resulting approach provides a reproducible ensemble benchmark against which to evaluate more complex methods. Results indicate that simple regression techniques can perform well and outperform more complicated methods during seasons with low hourly variability, however ensemble methods show higher accuracy overall. The results also highlight the significant impact of extreme weather on forecast accuracy, demonstrating the importance of forecasting processes that are resilient to extreme weather.

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