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Open AccessArticle

Distributed Reconciliation in Day-Ahead Wind Power Forecasting

by Li Bai 1 and Pierre Pinson 2,*
1
Department of Energy, Systems, Territory and Construction Engineering, University of Pisa, 56122 Pisa, Italy
2
Centre for Electric Power and Energy, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
*
Author to whom correspondence should be addressed.
Energies 2019, 12(6), 1112; https://doi.org/10.3390/en12061112
Received: 27 February 2019 / Revised: 15 March 2019 / Accepted: 18 March 2019 / Published: 21 March 2019
(This article belongs to the Special Issue Digital Solutions for Energy Management and Power Generation)
With increasing renewable energy generation capacities connected to the power grid, a number of decision-making problems require some form of consistency in the forecasts that are being used as input. In everyday words, one expects that the sum of the power generation forecasts for a set of wind farms is equal to the forecast made directly for the power generation of that portfolio. This forecast reconciliation problem has attracted increased attention in the energy forecasting literature over the last few years. Here, we review the state of the art and its applicability to day-ahead forecasting of wind power generation, in the context of spatial reconciliation. After gathering some observations on the properties of the game-theoretical optimal projection reconciliation approach, we propose to readily rethink it in a distributed setup by using the Alternating Direction Method of Multipliers (ADMM). Three case studies are considered for illustrating the interest and performance of the approach, based on simulated data, the National Renewable Energy Labaratory (NREL) Wind Toolkit dataset, and a dataset for a number of geographically distributed wind farms in Sardinia, Italy. View Full-Text
Keywords: wind energy; hierarchical time-series; forecast reconciliation; distributed optimization wind energy; hierarchical time-series; forecast reconciliation; distributed optimization
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Bai, L.; Pinson, P. Distributed Reconciliation in Day-Ahead Wind Power Forecasting. Energies 2019, 12, 1112.

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