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Smart Meter Forecasting from One Minute to One Year Horizons

CRS4, Center for Advanced Studies, Research and Development in Sardinia, loc. Piscina Manna ed. 1, 09010 Pula (CA), Italy
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Energies 2018, 11(12), 3520; https://doi.org/10.3390/en11123520
Received: 20 November 2018 / Revised: 28 November 2018 / Accepted: 13 December 2018 / Published: 18 December 2018
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Abstract

The ability to predict consumption is an essential tool for the management of a power distribution network. The availability of an advanced metering infrastructure through smart meters makes it possible to produce consumption forecasts down to the level of the individual user and to introduce intelligence and control at every level of the grid. While aggregate load forecasting is a mature technology, single user forecasting is a more difficult problem to address due to the multiple factors affecting consumption, which are not always easily predictable. This work presents a hybrid machine learning methodology based on random forest (RF) and linear regression (LR) for the deterministic and probabilistic forecast of household consumption at different time horizons and resolutions. The approach is based on the separation of long term effects (RF) from short term ones (LR), producing deterministic and probabilistic forecasts. The proposed procedure is applied to a public dataset, achieving a deterministic forecast accuracy much higher than other methodologies, in all scenarios analyzed. This covers horizons of forecast from one minute to one year, and highlights the great added value provided by probabilistic forecasting. View Full-Text
Keywords: load forecasting; smart meter; time series forecasting; machine learning; energy prediction load forecasting; smart meter; time series forecasting; machine learning; energy prediction
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Massidda, L.; Marrocu, M. Smart Meter Forecasting from One Minute to One Year Horizons. Energies 2018, 11, 3520.

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