| Using SCADA data for wind turbine condition monitoring–a review J Tautz‐Weinert, SJ Watson IET Renewable Power Generation 11 (4), 382-394, 2017 | 411 | 2017 |
| Analyzing the impact of weather variables on monthly electricity demand CL Hor, SJ Watson, S Majithia IEEE transactions on power systems 20 (4), 2078-2085, 2005 | 390 | 2005 |
| Condition monitoring of the power output of wind turbine generators using wavelets SJ Watson, BJ Xiang, W Yang, PJ Tavner, CJ Crabtree IEEE transactions on energy conversion 25 (3), 715-721, 2010 | 280 | 2010 |
| Future emerging technologies in the wind power sector: A European perspective S Watson, A Moro, V Reis, C Baniotopoulos, S Barth, G Bartoli, F Bauer, ... Renewable and sustainable energy reviews 113, 109270, 2019 | 258 | 2019 |
| Short-term prediction of local wind conditions L Landberg, SJ Watson Boundary-Layer Meteorology 70 (1), 171-195, 1994 | 255 | 1994 |
| Comparison of electrical energy efficiency of atmospheric and high-pressure electrolysers A Roy, S Watson, D Infield International Journal of Hydrogen Energy 31 (14), 1964-1979, 2006 | 242 | 2006 |
| Reliability analysis for wind turbines with incomplete failure data collected from after the date of initial installation H Guo, S Watson, P Tavner, J Xiang Reliability Engineering & System Safety 94 (6), 1057-1063, 2009 | 213 | 2009 |
| Physics of failure approach to wind turbine condition based maintenance CS Gray, SJ Watson Wind Energy 13 (5), 395-405, 2010 | 197 | 2010 |
| Estimating the potential yield of small building‐mounted wind turbines MA Heath, JD Walshe, SJ Watson Wind Energy: An International Journal for Progress and Applications in Wind …, 2007 | 174 | 2007 |
| Assessment of condition monitoring techniques for offshore wind farms E Wiggelinkhuizen, T Verbruggen, H Braam, L Rademakers, J Xiang, ... | 159 | 2008 |
| Airborne wind energy resource analysis P Bechtle, M Schelbergen, R Schmehl, U Zillmann, S Watson Renewable energy 141, 1103-1116, 2019 | 136 | 2019 |
| Application of wind speed forecasting to the integration of wind energy into a large scale power system SJ Watson, L Landberg, JA Halliday IEE Proceedings-Generation, Transmission and Distribution 141 (4), 357-362, 1994 | 136 | 1994 |
| Monte Carlo simulation of residential electricity demand for forecasting maximum demand on distribution networks DHO McQueen, PR Hyland, SJ Watson IEEE Transactions on power systems 19 (3), 1685-1689, 2004 | 129 | 2004 |
| Wind turbine drivetrains: state-of-the-art technologies and future development trends AR Nejad, J Keller, Y Guo, S Sheng, H Polinder, S Watson, J Dong, Z Qin, ... Wind Energy Science 7 (1), 387-411, 2022 | 122 | 2022 |
| Daily load forecasting and maximum demand estimation using ARIMA and GARCH CL Hor, SJ Watson, S Majithia 2006 International conference on probabilistic methods applied to power …, 2006 | 121 | 2006 |
| The impact of the anomalous weather of 1995 on the UK economy S Subak, JP Palutikof, MD Agnew, SJ Watson, CG Bentham, ... Climatic change 44, 1-26, 2000 | 90 | 2000 |
| A new matrix method of predicting long-term wind roses with MCP JC Woods, SJ Watson Journal of Wind Engineering and Industrial Aerodynamics 66 (2), 85-94, 1997 | 80 | 1997 |
| Building knowledge for substation-based decision support using rough sets CL Hor, PA Crossley, SJ Watson IEEE Transactions on Power Delivery 22 (3), 1372-1379, 2007 | 79 | 2007 |
| Quantifying the variability of wind energy S Watson Wiley Interdisciplinary Reviews: Energy and Environment 3 (4), 330-342, 2014 | 73 | 2014 |
| An effective approach for rotor electrical asymmetry detection in wind turbine DFIGs RK Ibrahim, SJ Watson, S Djurović, CJ Crabtree IEEE Transactions on Industrial Electronics 65 (11), 8872-8881, 2018 | 65 | 2018 |
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