Smart Meter Forecasting from One Minute to One Year Horizons

Luca Massidda, Marino Marrocu
Energies, Volume 11, Number 12 - december 2018
Download the publication : energies-11-03520.pdf [2.3Mo]  
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.

BibTex references

  author       = {Massidda, L. and Marrocu, M.},
  title        = {Smart Meter Forecasting from One Minute to One Year Horizons},
  journal      = {Energies},
  number       = {12},
  volume       = {11},
  month        = {december},
  year         = {2018},
  publisher    = {MDPI},
  keywords     = {load forecasting, smart meter, time series forecasting, machine learning, energy prediction},
  doi          = {10.3390/en11123520},
  url          = {},

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