Xiaolei Liu

Dr. Xiaolei Liu

orcid

Research Focus & Metrics

Academic Career Length

17Years (2010-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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5 November 2013 in Geothermal Energy
Verified
Journal Article
Open access
PDF file
KBKatrin Breede
Katrin Breede
KDKhatia Dzebisashvili
Khatia Dzebisashvili
XLXiaolei Liu
Xiaolei Liu
GFGioia Falcone
Gioia Falcone
Enhanced (or engineered) geothermal systems (EGS) have evolved from the hot dry rock concept, implemented for the first time at Fenton Hill in 1977. This paper systematically reviews all of the EGS projects worldwide, based on the information available in the public domain. The projects are classified by country, reservoir type, depth, reservoir temperature, stimulation methods, associated seismicity, plant capacity and current status. Thirty five years on from the first EGS implementation, the geothermal community can benefit from the lessons learnt and take a more objective approach to the pros and cons of ‘conventional’ EGS systems.

1 July 2021 in Energy
Verified
Journal Article
Closed access
XLXiaolei Liu
Xiaolei Liu
LZLin Zi
Lin Zi
ZFZiming Feng
Ziming Feng

22 July 2020 in Energies
Verified
Journal Article
Open access
PDF file
SHshahram hanifi
shahram hanifi
XLXiaolei Liu
Xiaolei Liu
LZLin Zi
Lin Zi
Saeid LotfianSaeid Lotfian
Saeid Lotfian
The largest obstacle that suppresses the increase of wind power penetration within the power grid is uncertainties and fluctuations in wind speeds. Therefore, accurate wind power forecasting is a challenging task, which can significantly impact the effective operation of power systems. Wind power forecasting is also vital for planning unit commitment, maintenance scheduling and profit maximisation of power traders. The current development of cost-effective operation and maintenance methods for modern wind turbines benefits from the advancement of effective and accurate wind power forecasting approaches. This paper systematically reviewed the state-of-the-art approaches of wind power forecasting with regard to physical, statistical (time series and artificial neural networks) and hybrid methods, including factors that affect accuracy and computational time in the predictive modelling efforts. Besides, this study provided a guideline for wind power forecasting process screening, allowing the wind turbine/farm operators to identify the most appropriate predictive methods based on time horizons, input features, computational time, error measurements, etc. More specifically, further recommendations for the research community of wind power forecasting were proposed based on reviewed literature.



1 December 2021 in Renewable Energy
Verified
Journal Article
Closed access
PTPavlos Trizoglou
Pavlos Trizoglou
XLXiaolei Liu
Xiaolei Liu
ZLZi Lin
Zi Lin