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Sustainability 2018, 10(3), 576; https://doi.org/10.3390/su10030576

Forecasting Renewable Energy Consumption under Zero Assumptions

1
School of Management and Economics, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu 611731, China
2
School of Information and Software Engineering, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu 611731, China
*
Author to whom correspondence should be addressed.
Received: 29 November 2017 / Revised: 6 February 2018 / Accepted: 11 February 2018 / Published: 25 February 2018
(This article belongs to the Special Issue Sustainable Energy Development under Climate Change)
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Abstract

Renewable energy, as an environmentally friendly and sustainable source of energy, is key to realizing the nationally determined contributions of the United States (US) to the December 2015 Paris agreement. Policymakers in the US rely on energy forecasts to draft and implement cost-minimizing, efficient and realistic renewable and sustainable energy policies but the inaccuracies in past projections are considerably high. The inaccuracies and inconsistencies in forecasts are due to the numerous factors considered, massive assumptions and modeling flaws in the underlying model. Here, we propose and apply a machine learning forecasting algorithm devoid of massive independent variables and assumptions to model and forecast renewable energy consumption (REC) in the US. We employ the forecasting technique to make projections on REC from biomass (REC-BMs) and hydroelectric (HE-EC) sources for the 2009–2016 period. We find that, relative to reference case projections in Energy Information Administration’s Annual Energy Outlook 2008, projections based on our proposed technique present an enormous improvement up to ~138.26-fold on REC-BMs and ~24.67-fold on HE-EC; and that applying our technique saves the US ~2692.62PJ petajoules(PJ) on HE-EC and ~9695.09PJ on REC-BMs for the 8-year forecast period. The achieved high-accuracy is also replicable to other regions. View Full-Text
Keywords: renewable energy; total biomass energy consumption; hydroelectric power energy consumption; volatility; LSTM RNN; forecasting; zero assumptions; own-data-driven modeling renewable energy; total biomass energy consumption; hydroelectric power energy consumption; volatility; LSTM RNN; forecasting; zero assumptions; own-data-driven modeling
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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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Ma, J.; Oppong, A.; Acheampong, K.N.; Abruquah, L.A. Forecasting Renewable Energy Consumption under Zero Assumptions. Sustainability 2018, 10, 576.

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