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BeatLex: Summarizing and Forecasting Time Series with Patterns Read chapter Read first chapter

2017 | OriginalPaper | Chapter

PowerCast: Mining and Forecasting Power Grid Sequences

Authors : Hyun Ah Song, Bryan Hooi, Marko Jereminov, Amritanshu Pandey, Larry Pileggi, Christos Faloutsos

Published in: Machine Learning and Knowledge Discovery in Databases

Publisher: Springer International Publishing

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Abstract

What will be the power consumption of our institution at 8am for the upcoming days? What will happen to the power consumption of a small factory, if it wants to double (or half) its production? Technologies associated with the smart electrical grid are needed. Central to this process are algorithms that accurately model electrical load behavior, and forecast future electric power demand. However, existing power load models fail to accurately represent electrical load behavior in the grid. In this paper, we propose PowerCast, a novel domain-aware approach for forecasting the electrical power demand, by carefully incorporating domain knowledge. Our contributions are as follows: 1. Infusion of domain expert knowledge: We represent the time sequences using an equivalent circuit model, the “BIG” model, which allows for an intuitive interpretation of the power load, as the BIG model is derived from physics-based first principles. 2. Forecasting of the power load: Our PowerCast uses the BIG model, and provides (a) accurate prediction in multi-step-ahead forecasting, and (b) extrapolations, under what-if scenarios, such as variation in the demand (say, due to increase in the count of people on campus, or a decision to half the production in our factory etc.) 3. Anomaly detection: PowerCast can spot and, even explain, anomalies in the given time sequences. The experimental results based on two real world datasets of up to three weeks duration, demonstrate that PowerCast is able to forecast several steps ahead, with 59% error reduction, compared to the competitors. Moreover, it is fast, and scales linearly with the duration of the sequences.

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Metadata
Title
PowerCast: Mining and Forecasting Power Grid Sequences
Authors
Hyun Ah Song
Bryan Hooi
Marko Jereminov
Amritanshu Pandey
Larry Pileggi
Christos Faloutsos
Copyright Year
2017
Book
Machine Learning and Knowledge Discovery in Databases

Print ISBN: 978-3-319-71245-1

Electronic ISBN: 978-3-319-71246-8

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-71246-8_37

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