| 1 |
Machine learning methods to assist energy system optimization
Perera ATD, Wickramasinghe PU, Nik VM, Scartezzini JL
Applied Energy, 243, 191, 2019 |
| 2 |
Machine learning methods to assist energy system optimization
Perera ATD, Wickramasinghe PU, Nik VM, Scartezzini JL
Applied Energy, 243, 191, 2019 |
| 3 |
Comparative analysis of selected thermoelectric generators operating with wood-fired stove
Sornek K, Filipowicz M, Zoladek M, Kot R, Mikrut M
Energy, 166, 1303, 2019 |
| 4 |
Results of a literature review on methods for estimating buildings energy demand at district level
Ferrari S, Zagarella F, Caputo P, D'Amico A
Energy, 175, 1130, 2019 |
| 5 |
Energy, exergy, exergoeconomic and environmental (4E) analysis of a distributed generation solar-assisted CCHP (combined cooling, heating and power) gas turbine system
Wang JJ, Lu ZR, Li M, Lior N, Li WH
Energy, 175, 1246, 2019 |
| 6 |
A comparison of methods for the optimal design of Distributed Energy Systems under uncertainty
Karmellos M, Georgiou PN, Mavrotas G
Energy, 178, 318, 2019 |
| 7 |
Multi-objective optimization and comparison framework for the design of Distributed Energy Systems
Karmellos M, Mavrotas G
Energy Conversion and Management, 180, 473, 2019 |
| 8 |
Design of distributed energy systems under uncertainty: A two-stage stochastic programming approach
Mavromatidis G, Orehounig K, Carmeliet J
Applied Energy, 222, 932, 2018 |
| 9 |
Robust optimal design of distributed energy systems based on life-cycle performance analysis using a probabilistic approach considering uncertainties of design inputs and equipment degradations
Kang J, Wang SW
Applied Energy, 231, 615, 2018 |
| 10 |
Optimal dispatch and equipment sizing of a residential central utility plant for improving rooftop solar integration
Deetjen TA, Vitter JS, Reimers AS, Webber ME
Energy, 147, 1044, 2018 |
