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Energies 2017, 10(11), 1879; https://doi.org/10.3390/en10111879

Assessing the Quality of Natural Gas Consumption Forecasting: An Application to the Italian Residential Sector

Dipartimento di Ingegneria meccanica, energetica, gestionale e dei trasporti (DIME), University of Genoa, via All’Opera Pia 15 A, 16145 Genoa, Italy
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Received: 23 October 2017 / Revised: 10 November 2017 / Accepted: 13 November 2017 / Published: 16 November 2017
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Abstract

(1) Background: The present paper aims at estimating the quality of the forecasts obtained by using one equation models. In particular, the focus is on the effect that the explanatory variables have on the forecasted quantity. The analysis is performed on the long term natural gas consumption in the Italian residential sector, but the same methodology can be applied to other contexts; (2) Methods: Different ex ante knowledge scenarios are built by associating different levels of confidence to the same set of explanatory variables. Forecasting results, coming from a standard regression algorithm and confirmed by a Kalman filter, are analyzed by means of covariance matrix propagation to assess the quality of the provided estimates; (3) Results: The outcomes show that one-equation models are very sensitive to the quality of the explanatory variables, therefore their erroneous estimation may have a relevant detrimental effect on the predictive accuracy of the model; (4) Conclusions: The overall ex ante forecasting accuracy of an example of one equation model is assessed. It has emerged that long-term forecasts need particular attention when the covered time horizon spans over decades. The information contained in the present paper is of interest for energy planners, supply network managers and policy makers in order to support their decisions. View Full-Text
Keywords: primary energy; energy demand; housing sector; error covariance; Kalman filter primary energy; energy demand; housing sector; error covariance; Kalman filter
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Scarpa, F.; Bianco, V. Assessing the Quality of Natural Gas Consumption Forecasting: An Application to the Italian Residential Sector. Energies 2017, 10, 1879.

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