| 1 |
A novel deep neural network based on randomly occurring distributed delayed PSO algorithm for monitoring the energy produced by four dual-axis solar trackers
Jallal MA, Chabaa S, Zeroual A
Renewable Energy, 149, 1182, 2020 |
| 2 |
Deep learning-based feature engineering methods for improved building energy prediction
Fan C, Sun YJ, Zhao Y, Song MJ, Wang JY
Applied Energy, 240, 35, 2019 |
| 3 |
Power variability of tidal-stream energy and implications for electricity supply
Lewis M, McNaughton J, Marquez-Dominguez C, Todeschini G, Togneri M, Masters I, Allmark M, Stallard T, Neill S, Goward-Brown A, Robins P
Energy, 183, 1061, 2019 |
| 4 |
Comparative study of data driven methods in building electricity use prediction
Zeng AR, Liu S, Yu Y
Energy and Buildings, 194, 289, 2019 |
| 5 |
A novel and effective nonlinear interpolation virtual sample generation method for enhancing energy prediction and analysis on small data problem: A case study of Ethylene industry
He YL, Wang PJ, Zhang MQ, Zhu QX, Xu Y
Energy, 147, 418, 2018 |
| 6 |
Potential of three variant machine-learning models for forecasting district level medium-term and long-term energy demand in smart grid environment
Ahmad T, Chen HX
Energy, 160, 1008, 2018 |
| 7 |
A hybrid approach to thermal building modelling using a combination of Gaussian processes and grey-box models
Gray FM, Schmidt M
Energy and Buildings, 165, 56, 2018 |
| 8 |
Multi-timescale power and energy assessment of lithium-ion battery and supercapacitor hybrid system using extended Kalman filter
Wang YJ, Zhang X, Liu C, Pan R, Chen ZH
Journal of Power Sources, 389, 93, 2018 |
| 9 |
Predicting solar energy generation through artificial neural networks using weather forecasts for microgrid control
Rodriguez F, Fleetwood A, Galarza A, Fontan L
Renewable Energy, 126, 855, 2018 |
| 10 |
A data-driven predictive model of city-scale energy use in buildings
Kontokosta CE, Tull C
Applied Energy, 197, 303, 2017 |
