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Forecasting and Risk Management for Renewable Energy

FRM 2017: Renewable Energy: Forecasting and Risk Management pp 137–145Cite as

A Flexible Mixed Additive-Multiplicative Model for Load Forecasting in a Smart Grid Setting

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Part of the book series: Springer Proceedings in Mathematics & Statistics ((PROMS,volume 254))

Abstract

This paper presents a mixed additive-multiplicative model for load forecasting that can be flexibly adapted to accommodate various forecasting needs in a Smart Grid setting. The flexibility of the model allows forecasting the load at different levels: system level, transform substation level, and feeder level. It also enables us to conduct short-term, medium and long-term load forecasting. The model decomposes load into two additive parts. One is independent of weather but dependent on the day of the week (d) and hour of the day (h), denoted as \(L_0(d,h)\). The other is the product of a weather-independent normal load, \(L_1(d,h)\), and weather-dependent factor, f(w). Weather information (w) includes the ambient temperature, relative humidity and their lagged versions. This method has been evaluated on real data for system level, transformer level and feeder level in the Northeastern part of the USA. Unlike many other forecasting methods, this method does not suffer from the accumulation and propagation of errors from prior hours.

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Author information

Authors and Affiliations

  1. Department of Applied Mathematics & Statistics and Advanced Energy Center, Stony Brook University, Stony Brook, NY, 11794, USA

    Eugene A. Feinberg & Jun Fei

Authors
  1. Eugene A. Feinberg
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  2. Jun Fei
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Corresponding author

Correspondence to Eugene A. Feinberg .

Editor information

Editors and Affiliations

  1. Laboratoire de Méteorologie Dynamique, CNRS, Palaiseau, France

    Philippe Drobinski

  2. UFR de Mathématiques, Université Paris Diderot, Paris, France

    Mathilde Mougeot

  3. UFR de Mathématiques, Université Paris Diderot, Paris, France

    Dominique Picard

  4. Laboratoire de Météorologie Dynamique, Ecole Polytechnique, Palaiseau, France

    Riwal Plougonven

  5. CREST—ENSAE Paris Tech, Palaiseau, France

    Peter Tankov

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Feinberg, E.A., Fei, J. (2018). A Flexible Mixed Additive-Multiplicative Model for Load Forecasting in a Smart Grid Setting. In: Drobinski, P., Mougeot, M., Picard, D., Plougonven, R., Tankov, P. (eds) Renewable Energy: Forecasting and Risk Management. FRM 2017. Springer Proceedings in Mathematics & Statistics, vol 254. Springer, Cham. https://doi.org/10.1007/978-3-319-99052-1_7

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