| 1 |
FlotationNet: A hierarchical deep learning network for froth flotation recovery prediction
Pu YY, Szmigiel A, Chen J, Apel DB
Powder Technology, 375, 317, 2020 |
| 2 |
Hydroelectricity consumption forecast for Pakistan using ARIMA modeling and supply-demand analysis for the year 2030
Jamil R
Renewable Energy, 154, 1, 2020 |
| 3 |
Will Trump's coal revival plan work? - Comparison of results based on the optimal combined forecasting technique and an extended IPAT forecasting technique
Wang Q, Li SY, Li RR
Energy, 169, 762, 2019 |
| 4 |
Short-term load forecasting by using a combined method of convolutional neural networks and fuzzy time series
Sadaei HJ, Silva PCDE, Guimaraes FG, Lee MH
Energy, 175, 365, 2019 |
| 5 |
Can semi-parametric additive models outperform linear models, when forecasting indoor temperatures in free-running buildings?
Gustin M, McLeod RS, Lomas KJ
Energy and Buildings, 193, 250, 2019 |
| 6 |
Artificial bee colony Based Bayesian Regularization Artificial Neural Network approach to model transient flammable cloud dispersion in congested area
Shi JH, Li XH, Khan F, Chang YJ, Zhu Y, Chen GM
Process Safety and Environmental Protection, 128, 121, 2019 |
| 7 |
Forecasting energy consumption time series using machine learning techniques based on usage patterns of residential householders
Chou JS, Tran DS
Energy, 165, 709, 2018 |
| 8 |
Solar irradiation prediction with machine learning: Forecasting models selection method depending on weather variability
Fouilloy A, Voyant C, Notton G, Motte F, Paoli C, Nivet ML, Guillot E, Duchaud JL
Energy, 165, 620, 2018 |
| 9 |
Wind speed forecasting approach based on Singular Spectrum Analysis and Adaptive Neuro Fuzzy Inference System
Moreno SR, Coelho LD
Renewable Energy, 126, 736, 2018 |
| 10 |
Forecasting method for global radiation time series without training phase: Comparison with other well-known prediction methodologies
Voyant C, Motte F, Fouilloy A, Notton G, Paoli C, Nivet ML
Energy, 120, 199, 2017 |
