Machine learning in energy economics and finance: A review
Abstract Machine learning (ML) is generating new opportunities for innovative research in
energy economics and finance. We critically review the burgeoning literature dedicated to …
energy economics and finance. We critically review the burgeoning literature dedicated to …
Forecasting methods in energy planning models
KB Debnath, M Mourshed - Renewable and Sustainable Energy Reviews, 2018 - Elsevier
Energy planning models (EPMs) play an indispensable role in policy formulation and energy
sector development. The forecasting of energy demand and supply is at the heart of an EPM …
sector development. The forecasting of energy demand and supply is at the heart of an EPM …
Underestimated impact of the COVID-19 on carbon emission reduction in developing countries–a novel assessment based on scenario analysis
Existing studies on the impact of the COVID-19 pandemic on carbon emissions are mainly
based on inter-annual change rate of carbon emissions. This study provided a new way to …
based on inter-annual change rate of carbon emissions. This study provided a new way to …
Forecasting mid-long term electric energy consumption through bagging ARIMA and exponential smoothing methods
EM de Oliveira, FLC Oliveira - Energy, 2018 - Elsevier
In the last decades, the world's energy consumption has increased rapidly due to
fundamental changes in the industry and economy. In such terms, accurate demand …
fundamental changes in the industry and economy. In such terms, accurate demand …
Improving renewable energy policy planning and decision-making through a hybrid MCDM method
Shifting from fossil to clean energy sources is a major global challenge, but in particular for
those countries with substantial fossil-fuel reserves and economies depending on fossil-fuel …
those countries with substantial fossil-fuel reserves and economies depending on fossil-fuel …
Short-term residential load forecasting: Impact of calendar effects and forecast granularity
Literature is rich in methodologies for “aggregated” load forecasting which has helped
electricity network operators and retailers in optimal planning and scheduling. The recent …
electricity network operators and retailers in optimal planning and scheduling. The recent …
Conventional models and artificial intelligence-based models for energy consumption forecasting: A review
Conventional models and artificial intelligence (AI)-based models have been widely applied
for energy consumption forecasting over the past decades. This paper reviews conventional …
for energy consumption forecasting over the past decades. This paper reviews conventional …
Forecasting energy consumption using ensemble ARIMA–ANFIS hybrid algorithm
S Barak, SS Sadegh - International Journal of Electrical Power & Energy …, 2016 - Elsevier
Energy consumption is on the rise in developing economies. In order to improve present and
future energy supplies, forecasting energy demands is essential. However, lack of accurate …
future energy supplies, forecasting energy demands is essential. However, lack of accurate …
A systematic review of statistical and machine learning methods for electrical power forecasting with reported mape score
Electric power forecasting plays a substantial role in the administration and balance of
current power systems. For this reason, accurate predictions of service demands are needed …
current power systems. For this reason, accurate predictions of service demands are needed …
Improving the Bi-LSTM model with XGBoost and attention mechanism: A combined approach for short-term power load prediction
Y Dai, Q Zhou, M Leng, X Yang, Y Wang - Applied Soft Computing, 2022 - Elsevier
Short term power load forecasting plays an important role in the management and
development of power systems with a focus on the reduction in power wastes and economic …
development of power systems with a focus on the reduction in power wastes and economic …