A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Forecasting Renewable Energy Consumption under Zero Assumptions
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.
Forecasting Renewable Energy Consumption under Zero Assumptions
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.
Forecasting Renewable Energy Consumption under Zero Assumptions
Jie Ma (author) / Amos Oppong (author) / Kingsley Nketia Acheampong (author) / Lucille Aba Abruquah (author)
2018
Article (Journal)
Electronic Resource
Unknown
renewable energy , total biomass energy consumption , hydroelectric power energy consumption , volatility , LSTM RNN , forecasting , zero assumptions , own-data-driven modeling , Environmental effects of industries and plants , TD194-195 , Renewable energy sources , TJ807-830 , Environmental sciences , GE1-350
Metadata by DOAJ is licensed under CC BY-SA 1.0
Forecasting Housing Prices under Different Market Segmentation Assumptions
| Online Contents | 2009
Weather Forecasting for Renewable Energy System: A Review
| Online Contents | 2022