A bottom-up methodology for long term electricity consumption forecasting of an industrial sector - Application to pulp and paper sector in Brazil
Long term annual electricity consumption forecasting is very important for country's energy planning. These forecasts are influenced by several factors (political, technological, social, environmental and economic), and brings with itself a high uncertainty degree in its results and difficultie...
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Veröffentlicht in: | Energy (Oxford) 2018-02, Vol.144, p.1107-1118 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | Long term annual electricity consumption forecasting is very important for country's energy planning. These forecasts are influenced by several factors (political, technological, social, environmental and economic), and brings with itself a high uncertainty degree in its results and difficulties in the evaluation of such factors over them. A methodology that eases to take into account these factors aiming improve the results and help understanding the electricity consumption annual trajectory till the forecast horizon is, therefore, very much useful and desired. So, we propose a modelling structure using the bottom-up approach to cope with these matters and to evaluate the trajectory of long term annual electricity consumption of a sector of the Brazilian industry up to 2050 considering energy efficiency (EE) scenarios. It is important to emphasize that Brazil is a developing country, and to build a bottom-up approach was a challenge, mainly due to the fact that this model is data intensive. In particular, this modelling was applied in the pulp and paper sector. The main goal was to consider technological diffusion scenarios in EE measures, and show the energy savings achieved. The results point an energy savings in the order of 25% when an actual scenario is considered.
•We propose a bottom-up forecasting model for the pulp and paper industry sector.•We detail the full mathematical formulation of the bottom-up approach.•We propose new methods to overcome the lack of data required by bottom-up approach.•The results point energy saving in the order of 25% at the forecast horizon. |
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ISSN: | 0360-5442 1873-6785 |
DOI: | 10.1016/j.energy.2017.12.078 |