Professor Jethro Browell

  • Professor of Statistics and Data Analytics (Statistics)

Biography

Jethro received the M.Phys. degree in Mathematics and Theoretical Physics from the University of St Andrews in 2011, and the Ph.D. degree in wind energy systems from the University of Strathclyde in 2015. He was a member of research and academic staff at Strathclyde from 2015 to 2021, during which time he held established the Energy Forecasting Research group and held an EPSRC Innovation Fellowship titled "System-wide probabilistic energy forecasting".

In 2021 Jethro joined the School of Mathematics and Statistics at the University of Glasgow as a Senior Lecturer in Statistics, and is currently seconded into the Energy Forecasting Team at National Grid ESO. He is an active member of the IEEE Power & Energy Society and is currently Chair of their Working Group on Energy Forecasting and Analytics. He is also a Fellow of the Royal Statistical Society, and active in the International Institute of Forecasters and International Energy Agency Wind Task 51 (forecasting).

Jethro has worked extensively with the energy industry in the UK and Europe developing methods for modelling and forecasting wind power production and electricity demand amongst other things. Several electricity generators and network operators are using forecasts and decision-support produced by his tools today.

Research interests

My core research interest is the production and use of probabilistic forecasts, those which quantify uncertainty. This includes development of novel forecasting methodologies, forecast evaluation, and the use of forecasts in decision-making, particularly in the energy sector. More generally I'm interested in regression/supervised learning, time series analysis, and decision-making under risk and uncertainty. I often work at the intersection of statistics, engineering, and meteorology, and have participated in and organised several forecasting competitions (recently HEFTcom).

My expertise may be summarised as:

  • Probabilistic forecasting: methodology and evaluation
  • Decision-making under uncertainty
  • Time series: classical and machine learning methods
  • Energy forecasting: wind, solar, demand, prices and more
  • Use of forecasts in energy trading and power system operation

Research groups

Publications

List by: Type | Date

Jump to: 2024 | 2023 | 2022 | 2021 | 2020 | 2019 | 2018 | 2017 | 2016 | 2015 | 2014 | 2013
Number of items: 64.

2024

Schneider, M. J., Browell, J. and Rankin, R. (2024) Limiting extreme behavior in forecasting competitions. Foresight, 75(Q4), pp. 32-37.

de Vilmarest, J., Browell, J. , Fasiolo, M., Goude, Y. and Wintenberger, O. (2024) Adaptive probabilistic forecasting of electricity (net-)load. IEEE Transactions on Power Systems, 39(2), pp. 4154-4163. (doi: 10.1109/TPWRS.2023.3310280)

2023

Donaldson, D. L., Browell, J. and Gilbert, C. (2023) Predicting the magnitude and timing of peak electricity demand: A competition case study. IET Smart Grid, (doi: 10.1049/stg2.12152) (Early Online Publication)

Gioia, V., Fasiolo, M., Browell, J. and Bellio, R. (2023) Additive covariance matrix models: modelling regional electricity net-demand in Great Britain. arXiv, (doi: 10.48550/ARXIV.2211.07451) (Unpublished)

Gilbert, C., Browell, J. and Stephen, B. (2023) Probabilistic load forecasting for the low voltage network: forecast fusion and daily peaks. Sustainable Energy, Grids and Networks, 34, 100998. (doi: 10.1016/j.segan.2023.100998)

Hu, M., Stephen, B., Browell, J. , Haben, S. and Wallom, D. C. H. (2023) Impacts of building load dispersion level on its load forecasting accuracy: Data or algorithms? Importance of reliability and interpretability in machine learning. Energy and Buildings, 285, 112896. (doi: 10.1016/j.enbuild.2023.112896)

2022

Browell, J. , Gilbert, C. and Fasiolo, M. (2022) Covariance structures for high-dimensional energy forecasting. Electric Power Systems Research, 211, 108446. (doi: 10.1016/j.epsr.2022.108446)

Heylen, E., Browell, J. and Teng, F. (2022) Probabilistic day-ahead inertia forecasting. IEEE Transactions on Power Systems, 37(5), pp. 3738-3746. (doi: 10.1109/TPWRS.2021.3134811)

Huppmann, D., Browell, J. , Nastasi, B., Vale, Z. and Süsser, D. (2022) A research agenda for open energy science: Opportunities and perspectives of the F1000Research Energy Gateway. F1000Research, 11, 896. (doi: 10.12688/f1000research.124267.1) (PMID:35967971) (PMCID:PMC9353196)

Craig, M. T. et al. (2022) Overcoming the disconnect between energy system and climate modeling. Joule, 6(7), pp. 1405-1417. (doi: 10.1016/j.joule.2022.05.010)

Petropoulos, F. et al. (2022) Forecasting: theory and practice. International Journal of Forecasting, 38(3), pp. 705-871. (doi: 10.1016/j.ijforecast.2021.11.001)

Tawn, R., Browell, J. and McMillan, D. (2022) Subseasonal-to-seasonal forecasting for wind turbine maintenance scheduling. Wind, 2(2), pp. 260-287. (doi: 10.3390/wind2020015)

White, C. J. et al. (2022) Advances in the application and utility of subseasonal-to-seasonal predictions. Bulletin of the American Meteorological Society, 103(6), E1448-E1472. (doi: 10.1175/BAMS-D-20-0224.1)

Browell, J. and Gilbert, C. (2022) Predicting electricity imbalance prices and volumes: capabilities and opportunities. Energies, 15(10), 3645. (doi: 10.3390/en15103645)

Graham, R. M., Browell, J. , Bertram, D. and White, C. J. (2022) The application of sub‐seasonal to seasonal (S2S) predictions for hydropower forecasting. Meteorological Applications, 29(1), e2047. (doi: 10.1002/met.2047)

Tawn, R. and Browell, J. (2022) A review of very short-term wind and solar power forecasting. Renewable and Sustainable Energy Reviews, 153, 111758. (doi: 10.1016/j.rser.2021.111758)

Farrokhabadi, M., Browell, J. , Wang, Y., Makonin, S., Su, W. and Zareipour, H. (2022) Day-ahead electricity demand forecasting competition: post-COVID paradigm. IEEE Open Access Journal of Power and Energy, 9, pp. 185-191. (doi: 10.1109/OAJPE.2022.3161101)

2021

Browell, J. and Fasiolo, M. (2021) Probabilistic forecasting of regional net-load with conditional extremes and gridded NWP. IEEE Transactions on Smart Grid, 12(6), pp. 5011-5019. (doi: 10.1109/TSG.2021.3107159)

Medina-Lopez, E. et al. (2021) Satellite data for the offshore renewable energy sector: synergies and innovation opportunities. Remote Sensing of Environment, 264, 112588. (doi: 10.1016/j.rse.2021.112588)

Telford, R., Stephen, B., Browell, J. and Haben, S. (2021) Dirichlet sampled capacity and loss estimation for LV distribution networks with partial observability. IEEE Transactions on Power Delivery, 36(5), pp. 2676-2686. (doi: 10.1109/TPWRD.2020.3025125)

Graham, R. M., Browell, J. , Bertram, D. and White, C. J. (2021) Developing a Sub-seasonal Forecasting System for Hydropower Reservoirs in Scotland. EGU General Assembly 2021, Online, 19-30 Apr 2021. (doi: 10.5194/egusphere-egu21-7252)

Bloomfield, H. C. et al. (2021) The importance of weather and climate to energy systems: a workshop on next generation challenges in energy–climate modeling. Bulletin of the American Meteorological Society, 102(1), E159-E167. (doi: 10.1175/BAMS-D-20-0256.1)

Gilbert, C., Browell, J. and McMillan, D. (2021) Probabilistic access forecasting for improved offshore operations. International Journal of Forecasting, 37(1), pp. 134-150. (doi: 10.1016/j.ijforecast.2020.03.007)

2020

Nedd, M., Browell, J. , Egea-Alvarez, A., Bell, K., Hamilton, R., Wang, S. and Brush, S. (2020) Operating a Zero Carbon GB Power System in 2025: Frequency and Fault Current [Annexes - Review of System and Network Issues, Frequency Stability, Power Electronic Devices and Fault Current, & Market Needs]. Project Report. University of Strathclyde, Glasgow. (doi: 10.17868/74793).

Nedd, M., Browell, J. , Egea-Alvarez, A., Bell, K., Hamilton, R., Wang, S. and Brush, S. (2020) Operating a Zero-Carbon GB Power System: Implications for Scotland. Technical Report. ClimateXChange, Edinburgh. (doi: 10.7488/era/756).

Tawn, R., Browell, J. and Dinwoodie, I. (2020) Missing data in wind farm time series: properties and effect on forecasts. Electric Power Systems Research, 189, 106640. (doi: 10.1016/j.epsr.2020.106640)

Browell, J. , Gilbert, C., Tawn, R. and May, L. (2020) Quantile Combination for the EEM20 Wind Power Forecasting Competition. In: 2020 17th International Conference on the European Energy Market (EEM), Stockholm, Sweden, 16-18 Sep 2020, ISBN 9781728169194 (doi: 10.1109/EEM49802.2020.9221942)

Browell, J. and Gilbert, C. (2020) ProbCast: Open-source Production, Evaluation and Visualisation of Probabilistic Forecasts. In: 2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), 18-21 Aug 2020, ISBN 9781728128221 (doi: 10.1109/PMAPS47429.2020.9183441)

Gilbert, C., Browell, J. and McMillan, D. (2020) Leveraging turbine-level data for improved probabilistic wind power forecasting. IEEE Transactions on Sustainable Energy, 11(3), pp. 1152-1160. (doi: 10.1109/TSTE.2019.2920085)

Messner, J. W., Pinson, P., Browell, J. , Bjerregård, M. B. and Schicker, I. (2020) Evaluation of wind power forecasts – an up-to-date view. Wind Energy, 23(6), pp. 1461-1481. (doi: 10.1002/we.2497)

Nedd, M., Browell, J. , Bell, K. and Booth, C. (2020) Containing a credible loss to within frequency stability limits in a low inertia GB power system. IEEE Transactions on Industry Applications, 56(2), pp. 1031-1039. (doi: 10.1109/TIA.2019.2959996)

Sweeney, C., Bessa, R. J., Browell, J. and Pinson, P. (2020) The future of forecasting for renewable energy. WIREs Energy and Environment, 9(2), e365. (doi: 10.1002/wene.365)

2019

Edmunds, C., Martín-Martínez, S., Browell, J. , Gómez-Lázaro, E. and Galloway, S. (2019) On the participation of wind energy in response and reserve markets in Great Britain and Spain. Renewable and Sustainable Energy Reviews, 115, 109360. (doi: 10.1016/j.rser.2019.109360)

Browell, J. , Stock, A. and McMillan, D. (2019) Recommendation for the Evaluation of Wind Farm Power Available Signal Accuracy. Project Report. University of Strathclyde, Glasgow.

Gilbert, C., Browell, J. and McMillan, D. (2019) A Data-driven Vessel Motion Model for Offshore Access Forecasting. In: OCEANS 2019 - Marseille, Marseille, France, 17-20 Jun 2019, ISBN 9781728114507 (doi: 10.1109/OCEANSE.2019.8867176)

Browell, J. , Möhrlen, C., Zack, J. and Messner, J. W. (2019) IEA Wind Recommended Practices for Selecting Renewable Power Forecasting Solutions: Part 3: Evaluation of Forecasts and Forecast Solutions. 6th International Conference Energy and Meteorology, Copenhagen, Denmark, 24-27 Jun 2019.

Möhrlen, C., Zack, J., Lerner, J., Messner, J., Browell, J. , Collier, C., Tuohy, A., Sharp, J. and Westenholz, M. (2019) Recommended Practices for the Implementation of Wind Power Forecasting Solutions: Part 2: Designing and Executing Forecasting Benchmarks and Trials. Project Report. IEA Wind.

Möhrlen, C., Zack, J., Messner, J., Browell, J. and Collier, C. (2019) Recommended Practices for the Implementation of Wind Power Forecasting Solutions: Part 3: Evaluation of Forecasts and Forecast Solutions. Project Report. IEA Wind.

2018

Browell, J. , Drew, D.R. and Philippopoulos, K. (2018) Improved very-short-term wind forecasting using atmospheric regimes. Wind Energy, 21(11), pp. 968-979. (doi: 10.1002/we.2207)

Möhrlen, C., Lerner, J., Messner, J. W., Browell, J. , Tuohy, A., Zack, J., Collier, C. and Giebel, G. (2018) IEA Wind Recommended Practices for the Implementation of Wind Power Forecasting Solutions Part 2 and 3: Designing and Executing Forecasting Benchmarks and Evaluation of Forecast Solutions. 17th Wind Integration Workshop, Stockholm, Sweden, 17-19 Oct 2018.

Gilbert, C., Browell, J. and McMillan, D. (2018) A Hierarchical Approach to Probabilistic Wind Power Forecasting. In: 2018 IEEE International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), Boise, ID, USA, 24-28 Jun 2018, ISBN 9781538635964 (doi: 10.1109/PMAPS.2018.8440571)

Browell, J. (2018) Risk constrained trading strategies for stochastic generation with a single-price balancing market. Energies, 11(6), 1345. (doi: 10.3390/en11061345)

2017

Bessa, R., Möhrlen, C., Fundel, V., Siefert, M., Browell, J. , Haglund El Gaidi, S., Hodge, B.-M., Cali, U. and Kariniotakis, G. (2017) Towards improved understanding of the applicability of uncertainty forecasts in the electric power industry. Energies, 10(9), 1402. (doi: 10.3390/en10091402)

Malvaldi, A., Weiss, S., Infield, D., Browell, J. , Leahy, P. and Foley, A.M. (2017) A spatial and temporal correlation analysis of aggregate wind power in an ideally interconnected Europe. Wind Energy, 20(8), pp. 1315-1329. (doi: 10.1002/we.2095)

Browell, J. and Gilbert, C. (2017) Cluster-based Regime-switching AR for the EEM 2017 Wind Power Forecasting Competition. In: 2017 14th International Conference on the European Energy Market (EEM), Dresden, Germany, 06-09 Jun 2017, ISBN 9781509054992 (doi: 10.1109/EEM.2017.7982034)

Cavalcante, L., Bessa, R. J., Reis, M. and Browell, J. (2017) LASSO vector autoregression structures for very short-term wind power forecasting. Wind Energy, 20(4), pp. 657-675. (doi: 10.1002/we.2029)

Browell, J. , Gilbert, C. and McMillan, D. (2017) Use of Turbine-level Data for Improved Wind Power Forecasting. In: 2017 IEEE Manchester PowerTech, Manchester, UK, 18-22 Jun 2017, ISBN 9781509042371 (doi: 10.1109/PTC.2017.7981134)

McMillan, D. and Browell, J. (2017) Optimisation of Wind Energy O&M Decision Making Under Uncertainty [Final Report]: Exploitation Plan. Project Report. University of Strathclyde, Glasgow.

2016

Dowell, J. , Hawker, G., Bell, K. and Gill, S. (2016) A Review of Probabilistic Methods for Defining Reserve Requirements. In: 2016 IEEE Power and Energy Society General Meeting (PESGM), Boston, MA, USA, 17-21 Jul 2016, ISBN 9781509041688 (doi: 10.1109/PESGM.2016.7741361)

Browell, J. , Dinwoodie, I. and McMillan, D. (2016) Forecasting for Day-ahead Offshore Maintenance Scheduling Under Uncertainty. In: 26th European Safety and Reliability Conference (ESREL 2016), Glasgow, Scotland, 25-29 Sep 2016, pp. 1137-1144. ISBN 9781138029972 (doi: 10.1201/9781315374987-16)

Bessa, R. J., Dowell, J. and Pinson, P. (2016) Renewable energy forecasting. In: Liu, C.-C., McArthur, S. and Lee, S.-J. (eds.) Smart Grid Handbook. John Wiley and Sons Ltd: Chichester, West Sussex, United Kingdom. ISBN 9781118755488 (doi: 10.1002/9781118755471.sgd050)

Browell, J. (2016) Forecasting Electricity Prices and Market Length for Trading Stochastic Generation in Markets With a Single-price Balancing Mechanism. In: 36th International Symposium on Forecasting, Santander, Spain, 19-22 Jun 2016,

Dowell, J. and Pinson, P. (2016) Very-short-term probabilistic wind power forecasts by sparse vector autoregression. IEEE Transactions on Smart Grid, 7(2), pp. 763-770. (doi: 10.1109/TSG.2015.2424078)

Catterson, V.M., McMillan, D., Dinwoodie, I., Revie, M., Dowell, J. , Quigley, J. and Wilson, K. (2016) An economic impact metric for evaluating wave height forecasters for offshore wind maintenance access. Wind Energy, 19(2), pp. 199-212. (doi: 10.1002/we.1826)

2015

Malvaldi, A., Dowell, J. , Weiss, S. and Infield, D. (2015) Wind Prediction Enhancement by Exploiting Data Non-stationarity. In: 2nd IET International Conference on Intelligent Signal Processing 2015 (ISP), London, UK, 01-02 Dec 2015, ISBN 9781785611360 (doi: 10.1049/cp.2015.1795)

Dowell, J. , Weiss, S. and Infield, D. (2015) Kernel Methods for Short-term Spatio-temporal Wind Prediction. In: 2015 IEEE Power and Energy Society General Meeting, Denver, CO, USA, 26-30 Jul 2015, ISBN 9781467380409 (doi: 10.1109/PESGM.2015.7285965)

2014

Dowell, J. , Weiss, S., Hill, D. and Infield, D. (2014) Short-term spatio-temporal prediction of wind speed and direction. Wind Energy, 17(12), pp. 1945-1955. (doi: 10.1002/we.1682)

Malvaldi, A., Dowell, J. , Weiss, S., Infield, D. and Hill, D. (2014) Wind Prediction Enhancement by Supplementing Measurements with Numerical Weather Prediction Now-Casts. 10th EAWE PhD Seminar on Wind Energy in Europe, Orléans, France, 28-31 Oct 2014.

Dowell, J. , Weiss, S. and Infield, D. (2014) Spatio-temporal Prediction of Wind Speed and Direction by Continuous Directional Regime. In: 2014 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), Durham, UK, 07-10 Jul 2014, ISBN 9781479935611 (doi: 10.1109/PMAPS.2014.6960596)

Dowell, J. , Weiss, S., Infield, D. and Chandna, S. (2014) A Widely Linear Multichannel Wiener Filter for Wind Prediction. In: 2014 IEEE Workshop on Statistical Signal Processing (SSP), Gold Coast, QLD, Australia, 29 Jun - 02 Jul 2014, pp. 29-32. ISBN 9781479949755 (doi: 10.1109/SSP.2014.6884567)

2013

Dowell, J. and Weiss, S. (2013) Short-term Wind Prediction Using an Ensemble of Particle Swarm Optimised FIR Filters. In: IET Intelligent Signal Processing Conference 2013 (ISP 2013), London, UK, 02-03 Dec 2013, ISBN 9781849197748 (doi: 10.1049/cp.2013.2065)

Dowell, J. , Weiss, S., Hill, D. and Infield, D. (2013) A Cyclo-stationary Complex Multichannel Wiener Filter for the Prediction of Wind Speed and Direction. In: 21st European Signal Processing Conference, Marrakesh, Morocco, 09-13 Sep 2013, ISBN 9780992862602

Dowell, J. , Weiss, S., Hill, D. and Infield, D. (2013) Improved Spatial Modelling of Wind Fields. European Wind Energy Association, Vienna, Austria, 20 Feb 2013.

Dowell, J. , Zitrou, A., Walls, L., Bedford, T. and Infield, D. (2013) Analysis of Wind and Wave Data to Assess Maintenance Access to Offshore Wind Farms. In: European Safety and Reliability Conference (ESREL 2013), Amsterdam, The Netherlands, 29 Sep - 02 Oct 2013, pp. 743-750.

This list was generated on Thu Nov 21 03:26:06 2024 GMT.
Jump to: Articles | Book Sections | Research Reports or Papers | Conference or Workshop Item | Conference Proceedings
Number of items: 64.

Articles

Schneider, M. J., Browell, J. and Rankin, R. (2024) Limiting extreme behavior in forecasting competitions. Foresight, 75(Q4), pp. 32-37.

de Vilmarest, J., Browell, J. , Fasiolo, M., Goude, Y. and Wintenberger, O. (2024) Adaptive probabilistic forecasting of electricity (net-)load. IEEE Transactions on Power Systems, 39(2), pp. 4154-4163. (doi: 10.1109/TPWRS.2023.3310280)

Donaldson, D. L., Browell, J. and Gilbert, C. (2023) Predicting the magnitude and timing of peak electricity demand: A competition case study. IET Smart Grid, (doi: 10.1049/stg2.12152) (Early Online Publication)

Gioia, V., Fasiolo, M., Browell, J. and Bellio, R. (2023) Additive covariance matrix models: modelling regional electricity net-demand in Great Britain. arXiv, (doi: 10.48550/ARXIV.2211.07451) (Unpublished)

Gilbert, C., Browell, J. and Stephen, B. (2023) Probabilistic load forecasting for the low voltage network: forecast fusion and daily peaks. Sustainable Energy, Grids and Networks, 34, 100998. (doi: 10.1016/j.segan.2023.100998)

Hu, M., Stephen, B., Browell, J. , Haben, S. and Wallom, D. C. H. (2023) Impacts of building load dispersion level on its load forecasting accuracy: Data or algorithms? Importance of reliability and interpretability in machine learning. Energy and Buildings, 285, 112896. (doi: 10.1016/j.enbuild.2023.112896)

Browell, J. , Gilbert, C. and Fasiolo, M. (2022) Covariance structures for high-dimensional energy forecasting. Electric Power Systems Research, 211, 108446. (doi: 10.1016/j.epsr.2022.108446)

Heylen, E., Browell, J. and Teng, F. (2022) Probabilistic day-ahead inertia forecasting. IEEE Transactions on Power Systems, 37(5), pp. 3738-3746. (doi: 10.1109/TPWRS.2021.3134811)

Huppmann, D., Browell, J. , Nastasi, B., Vale, Z. and Süsser, D. (2022) A research agenda for open energy science: Opportunities and perspectives of the F1000Research Energy Gateway. F1000Research, 11, 896. (doi: 10.12688/f1000research.124267.1) (PMID:35967971) (PMCID:PMC9353196)

Craig, M. T. et al. (2022) Overcoming the disconnect between energy system and climate modeling. Joule, 6(7), pp. 1405-1417. (doi: 10.1016/j.joule.2022.05.010)

Petropoulos, F. et al. (2022) Forecasting: theory and practice. International Journal of Forecasting, 38(3), pp. 705-871. (doi: 10.1016/j.ijforecast.2021.11.001)

Tawn, R., Browell, J. and McMillan, D. (2022) Subseasonal-to-seasonal forecasting for wind turbine maintenance scheduling. Wind, 2(2), pp. 260-287. (doi: 10.3390/wind2020015)

White, C. J. et al. (2022) Advances in the application and utility of subseasonal-to-seasonal predictions. Bulletin of the American Meteorological Society, 103(6), E1448-E1472. (doi: 10.1175/BAMS-D-20-0224.1)

Browell, J. and Gilbert, C. (2022) Predicting electricity imbalance prices and volumes: capabilities and opportunities. Energies, 15(10), 3645. (doi: 10.3390/en15103645)

Graham, R. M., Browell, J. , Bertram, D. and White, C. J. (2022) The application of sub‐seasonal to seasonal (S2S) predictions for hydropower forecasting. Meteorological Applications, 29(1), e2047. (doi: 10.1002/met.2047)

Tawn, R. and Browell, J. (2022) A review of very short-term wind and solar power forecasting. Renewable and Sustainable Energy Reviews, 153, 111758. (doi: 10.1016/j.rser.2021.111758)

Farrokhabadi, M., Browell, J. , Wang, Y., Makonin, S., Su, W. and Zareipour, H. (2022) Day-ahead electricity demand forecasting competition: post-COVID paradigm. IEEE Open Access Journal of Power and Energy, 9, pp. 185-191. (doi: 10.1109/OAJPE.2022.3161101)

Browell, J. and Fasiolo, M. (2021) Probabilistic forecasting of regional net-load with conditional extremes and gridded NWP. IEEE Transactions on Smart Grid, 12(6), pp. 5011-5019. (doi: 10.1109/TSG.2021.3107159)

Medina-Lopez, E. et al. (2021) Satellite data for the offshore renewable energy sector: synergies and innovation opportunities. Remote Sensing of Environment, 264, 112588. (doi: 10.1016/j.rse.2021.112588)

Telford, R., Stephen, B., Browell, J. and Haben, S. (2021) Dirichlet sampled capacity and loss estimation for LV distribution networks with partial observability. IEEE Transactions on Power Delivery, 36(5), pp. 2676-2686. (doi: 10.1109/TPWRD.2020.3025125)

Bloomfield, H. C. et al. (2021) The importance of weather and climate to energy systems: a workshop on next generation challenges in energy–climate modeling. Bulletin of the American Meteorological Society, 102(1), E159-E167. (doi: 10.1175/BAMS-D-20-0256.1)

Gilbert, C., Browell, J. and McMillan, D. (2021) Probabilistic access forecasting for improved offshore operations. International Journal of Forecasting, 37(1), pp. 134-150. (doi: 10.1016/j.ijforecast.2020.03.007)

Tawn, R., Browell, J. and Dinwoodie, I. (2020) Missing data in wind farm time series: properties and effect on forecasts. Electric Power Systems Research, 189, 106640. (doi: 10.1016/j.epsr.2020.106640)

Gilbert, C., Browell, J. and McMillan, D. (2020) Leveraging turbine-level data for improved probabilistic wind power forecasting. IEEE Transactions on Sustainable Energy, 11(3), pp. 1152-1160. (doi: 10.1109/TSTE.2019.2920085)

Messner, J. W., Pinson, P., Browell, J. , Bjerregård, M. B. and Schicker, I. (2020) Evaluation of wind power forecasts – an up-to-date view. Wind Energy, 23(6), pp. 1461-1481. (doi: 10.1002/we.2497)

Nedd, M., Browell, J. , Bell, K. and Booth, C. (2020) Containing a credible loss to within frequency stability limits in a low inertia GB power system. IEEE Transactions on Industry Applications, 56(2), pp. 1031-1039. (doi: 10.1109/TIA.2019.2959996)

Sweeney, C., Bessa, R. J., Browell, J. and Pinson, P. (2020) The future of forecasting for renewable energy. WIREs Energy and Environment, 9(2), e365. (doi: 10.1002/wene.365)

Edmunds, C., Martín-Martínez, S., Browell, J. , Gómez-Lázaro, E. and Galloway, S. (2019) On the participation of wind energy in response and reserve markets in Great Britain and Spain. Renewable and Sustainable Energy Reviews, 115, 109360. (doi: 10.1016/j.rser.2019.109360)

Browell, J. , Drew, D.R. and Philippopoulos, K. (2018) Improved very-short-term wind forecasting using atmospheric regimes. Wind Energy, 21(11), pp. 968-979. (doi: 10.1002/we.2207)

Browell, J. (2018) Risk constrained trading strategies for stochastic generation with a single-price balancing market. Energies, 11(6), 1345. (doi: 10.3390/en11061345)

Bessa, R., Möhrlen, C., Fundel, V., Siefert, M., Browell, J. , Haglund El Gaidi, S., Hodge, B.-M., Cali, U. and Kariniotakis, G. (2017) Towards improved understanding of the applicability of uncertainty forecasts in the electric power industry. Energies, 10(9), 1402. (doi: 10.3390/en10091402)

Malvaldi, A., Weiss, S., Infield, D., Browell, J. , Leahy, P. and Foley, A.M. (2017) A spatial and temporal correlation analysis of aggregate wind power in an ideally interconnected Europe. Wind Energy, 20(8), pp. 1315-1329. (doi: 10.1002/we.2095)

Cavalcante, L., Bessa, R. J., Reis, M. and Browell, J. (2017) LASSO vector autoregression structures for very short-term wind power forecasting. Wind Energy, 20(4), pp. 657-675. (doi: 10.1002/we.2029)

Dowell, J. and Pinson, P. (2016) Very-short-term probabilistic wind power forecasts by sparse vector autoregression. IEEE Transactions on Smart Grid, 7(2), pp. 763-770. (doi: 10.1109/TSG.2015.2424078)

Catterson, V.M., McMillan, D., Dinwoodie, I., Revie, M., Dowell, J. , Quigley, J. and Wilson, K. (2016) An economic impact metric for evaluating wave height forecasters for offshore wind maintenance access. Wind Energy, 19(2), pp. 199-212. (doi: 10.1002/we.1826)

Dowell, J. , Weiss, S., Hill, D. and Infield, D. (2014) Short-term spatio-temporal prediction of wind speed and direction. Wind Energy, 17(12), pp. 1945-1955. (doi: 10.1002/we.1682)

Book Sections

Bessa, R. J., Dowell, J. and Pinson, P. (2016) Renewable energy forecasting. In: Liu, C.-C., McArthur, S. and Lee, S.-J. (eds.) Smart Grid Handbook. John Wiley and Sons Ltd: Chichester, West Sussex, United Kingdom. ISBN 9781118755488 (doi: 10.1002/9781118755471.sgd050)

Research Reports or Papers

Nedd, M., Browell, J. , Egea-Alvarez, A., Bell, K., Hamilton, R., Wang, S. and Brush, S. (2020) Operating a Zero Carbon GB Power System in 2025: Frequency and Fault Current [Annexes - Review of System and Network Issues, Frequency Stability, Power Electronic Devices and Fault Current, & Market Needs]. Project Report. University of Strathclyde, Glasgow. (doi: 10.17868/74793).

Nedd, M., Browell, J. , Egea-Alvarez, A., Bell, K., Hamilton, R., Wang, S. and Brush, S. (2020) Operating a Zero-Carbon GB Power System: Implications for Scotland. Technical Report. ClimateXChange, Edinburgh. (doi: 10.7488/era/756).

Browell, J. , Stock, A. and McMillan, D. (2019) Recommendation for the Evaluation of Wind Farm Power Available Signal Accuracy. Project Report. University of Strathclyde, Glasgow.

Möhrlen, C., Zack, J., Lerner, J., Messner, J., Browell, J. , Collier, C., Tuohy, A., Sharp, J. and Westenholz, M. (2019) Recommended Practices for the Implementation of Wind Power Forecasting Solutions: Part 2: Designing and Executing Forecasting Benchmarks and Trials. Project Report. IEA Wind.

Möhrlen, C., Zack, J., Messner, J., Browell, J. and Collier, C. (2019) Recommended Practices for the Implementation of Wind Power Forecasting Solutions: Part 3: Evaluation of Forecasts and Forecast Solutions. Project Report. IEA Wind.

McMillan, D. and Browell, J. (2017) Optimisation of Wind Energy O&M Decision Making Under Uncertainty [Final Report]: Exploitation Plan. Project Report. University of Strathclyde, Glasgow.

Conference or Workshop Item

Graham, R. M., Browell, J. , Bertram, D. and White, C. J. (2021) Developing a Sub-seasonal Forecasting System for Hydropower Reservoirs in Scotland. EGU General Assembly 2021, Online, 19-30 Apr 2021. (doi: 10.5194/egusphere-egu21-7252)

Browell, J. , Möhrlen, C., Zack, J. and Messner, J. W. (2019) IEA Wind Recommended Practices for Selecting Renewable Power Forecasting Solutions: Part 3: Evaluation of Forecasts and Forecast Solutions. 6th International Conference Energy and Meteorology, Copenhagen, Denmark, 24-27 Jun 2019.

Möhrlen, C., Lerner, J., Messner, J. W., Browell, J. , Tuohy, A., Zack, J., Collier, C. and Giebel, G. (2018) IEA Wind Recommended Practices for the Implementation of Wind Power Forecasting Solutions Part 2 and 3: Designing and Executing Forecasting Benchmarks and Evaluation of Forecast Solutions. 17th Wind Integration Workshop, Stockholm, Sweden, 17-19 Oct 2018.

Malvaldi, A., Dowell, J. , Weiss, S., Infield, D. and Hill, D. (2014) Wind Prediction Enhancement by Supplementing Measurements with Numerical Weather Prediction Now-Casts. 10th EAWE PhD Seminar on Wind Energy in Europe, Orléans, France, 28-31 Oct 2014.

Dowell, J. , Weiss, S., Hill, D. and Infield, D. (2013) Improved Spatial Modelling of Wind Fields. European Wind Energy Association, Vienna, Austria, 20 Feb 2013.

Conference Proceedings

Browell, J. , Gilbert, C., Tawn, R. and May, L. (2020) Quantile Combination for the EEM20 Wind Power Forecasting Competition. In: 2020 17th International Conference on the European Energy Market (EEM), Stockholm, Sweden, 16-18 Sep 2020, ISBN 9781728169194 (doi: 10.1109/EEM49802.2020.9221942)

Browell, J. and Gilbert, C. (2020) ProbCast: Open-source Production, Evaluation and Visualisation of Probabilistic Forecasts. In: 2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), 18-21 Aug 2020, ISBN 9781728128221 (doi: 10.1109/PMAPS47429.2020.9183441)

Gilbert, C., Browell, J. and McMillan, D. (2019) A Data-driven Vessel Motion Model for Offshore Access Forecasting. In: OCEANS 2019 - Marseille, Marseille, France, 17-20 Jun 2019, ISBN 9781728114507 (doi: 10.1109/OCEANSE.2019.8867176)

Gilbert, C., Browell, J. and McMillan, D. (2018) A Hierarchical Approach to Probabilistic Wind Power Forecasting. In: 2018 IEEE International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), Boise, ID, USA, 24-28 Jun 2018, ISBN 9781538635964 (doi: 10.1109/PMAPS.2018.8440571)

Browell, J. and Gilbert, C. (2017) Cluster-based Regime-switching AR for the EEM 2017 Wind Power Forecasting Competition. In: 2017 14th International Conference on the European Energy Market (EEM), Dresden, Germany, 06-09 Jun 2017, ISBN 9781509054992 (doi: 10.1109/EEM.2017.7982034)

Browell, J. , Gilbert, C. and McMillan, D. (2017) Use of Turbine-level Data for Improved Wind Power Forecasting. In: 2017 IEEE Manchester PowerTech, Manchester, UK, 18-22 Jun 2017, ISBN 9781509042371 (doi: 10.1109/PTC.2017.7981134)

Dowell, J. , Hawker, G., Bell, K. and Gill, S. (2016) A Review of Probabilistic Methods for Defining Reserve Requirements. In: 2016 IEEE Power and Energy Society General Meeting (PESGM), Boston, MA, USA, 17-21 Jul 2016, ISBN 9781509041688 (doi: 10.1109/PESGM.2016.7741361)

Browell, J. , Dinwoodie, I. and McMillan, D. (2016) Forecasting for Day-ahead Offshore Maintenance Scheduling Under Uncertainty. In: 26th European Safety and Reliability Conference (ESREL 2016), Glasgow, Scotland, 25-29 Sep 2016, pp. 1137-1144. ISBN 9781138029972 (doi: 10.1201/9781315374987-16)

Browell, J. (2016) Forecasting Electricity Prices and Market Length for Trading Stochastic Generation in Markets With a Single-price Balancing Mechanism. In: 36th International Symposium on Forecasting, Santander, Spain, 19-22 Jun 2016,

Malvaldi, A., Dowell, J. , Weiss, S. and Infield, D. (2015) Wind Prediction Enhancement by Exploiting Data Non-stationarity. In: 2nd IET International Conference on Intelligent Signal Processing 2015 (ISP), London, UK, 01-02 Dec 2015, ISBN 9781785611360 (doi: 10.1049/cp.2015.1795)

Dowell, J. , Weiss, S. and Infield, D. (2015) Kernel Methods for Short-term Spatio-temporal Wind Prediction. In: 2015 IEEE Power and Energy Society General Meeting, Denver, CO, USA, 26-30 Jul 2015, ISBN 9781467380409 (doi: 10.1109/PESGM.2015.7285965)

Dowell, J. , Weiss, S. and Infield, D. (2014) Spatio-temporal Prediction of Wind Speed and Direction by Continuous Directional Regime. In: 2014 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), Durham, UK, 07-10 Jul 2014, ISBN 9781479935611 (doi: 10.1109/PMAPS.2014.6960596)

Dowell, J. , Weiss, S., Infield, D. and Chandna, S. (2014) A Widely Linear Multichannel Wiener Filter for Wind Prediction. In: 2014 IEEE Workshop on Statistical Signal Processing (SSP), Gold Coast, QLD, Australia, 29 Jun - 02 Jul 2014, pp. 29-32. ISBN 9781479949755 (doi: 10.1109/SSP.2014.6884567)

Dowell, J. and Weiss, S. (2013) Short-term Wind Prediction Using an Ensemble of Particle Swarm Optimised FIR Filters. In: IET Intelligent Signal Processing Conference 2013 (ISP 2013), London, UK, 02-03 Dec 2013, ISBN 9781849197748 (doi: 10.1049/cp.2013.2065)

Dowell, J. , Weiss, S., Hill, D. and Infield, D. (2013) A Cyclo-stationary Complex Multichannel Wiener Filter for the Prediction of Wind Speed and Direction. In: 21st European Signal Processing Conference, Marrakesh, Morocco, 09-13 Sep 2013, ISBN 9780992862602

Dowell, J. , Zitrou, A., Walls, L., Bedford, T. and Infield, D. (2013) Analysis of Wind and Wave Data to Assess Maintenance Access to Offshore Wind Farms. In: European Safety and Reliability Conference (ESREL 2013), Amsterdam, The Netherlands, 29 Sep - 02 Oct 2013, pp. 743-750.

This list was generated on Thu Nov 21 03:26:06 2024 GMT.

Grants

Recent grants:

  • BBSRC: Using Demand Flexing to Transform Indoor Farms into Renewable Energy Assets [BB/Z514469/1, 2024-26]
  • Ofgem Strategic Innovation Fund: "Predict-4-Resilience" (Discovery, Alpha and Betea Phase, 2021-27)
  • EPSRC Innovation Fellowship: "System-wide probabilistic energy forecasting" [EP/R023484/1, 2018-22]
  • EPSRC: "Analytical Middleware for Informed Distribution Networks (AMIDiNe)" [EP/S030131/1, 2019-22]
  • EPSRC Supergen Energy Networks Hub: "Energy Forecasting for Market-led Multi-vector Networks" [SEN Hub Flex-fund, 2020-21]

 

Supervision

I welcome enquiries from prospective PhD students and industrial partners interested in any of the following or related topics. Please contact me directly in the first instance to discuss research interests, funding & scholarships, and life at Glasgow.

  • Probabilistic forecasting: high-dimensional and multivariate prediction, statistical methods for forecast production, evaluation and decision-support
  • Energy forecasting (wind power, solar power, energy demand, energy prices): method and applications for system operation and market participation
  • Statistical post-processing of numerical weather prediction: ensemble post-processing, sub-seasonal to seasonal forecasting, applications and decision-support

Current PhD Students:

  • Tao Shen: Adaptive probabilistic forecasting
  • Gabriel Dantas: Short-term forecast uncertainty in future low-carbon energy systems
  • Klimis Stylpnopoulos: Multi-variate forecasting for wind power integration in electricity markets (with Shell)
  • Panthakan Boonsuriyatham: Forecasting local net-electricity demand at scale

Past PhD Students:

  • Rosemary Tawn: Predictive Analytics for Short-term Wind and Solar Power Forecasting (with Natural Power)
  • Ciaran Gilbert: Topics in High-Dimensional Energy Forecasting

Teaching

My current teaching duties include:

  • Honours, MSci and MSc project supervision
  • Time Series (Honours and Masters level)
  • MSci Work Placement Coordinator

Professional activities & recognition

Prizes, awards & distinctions

  • 2015: One year of independent post-doctoral research at the University of Strathclyde (EPSRC Doctoral Prize)
  • : Multiple high placings in energy forecasting competitions, including EEM2017 (winner) & 2020, GEFcom2017 and BigDEAL 2022 (Various)

Research fellowships

  • 2018 - 2021: EPSRC Innovation Fellowship

Grant committees & research advisory boards

  • 2021: EPSRC, Peer Review College

Editorial boards

  • 2022: IEEE Access
  • 2021: Sustainable Energy, Grids and Networks
  • 2020: Renewable and Sustainable Energy Reviews

Professional & learned societies

  • 2016: Chair (2022-), Vice-chair (2021-22), IEEE PES Working Group on Energy Forecasting and Analytics
  • 2016: Vice-chair (2022-), Secretary (2020-22), UK Chapter, International Institute of Forecasters
  • 2015: Senior Member, IEEE (The Institute of Electrical and Electronics Engineers)
  • 2017: Fellow, Royal Statistical Society
  • 2017: Fellow, Higher Education Academy / Advance HE

Supplementary

  • Jethro has competed in a number of energy forecasting competitions as a member of teams which won the EEM2017 wind power forecasting competition, and placed highly in EEM2020, OREC Hackathon (2019) and GEFcom2017.

Additional information

Links to pre-prints/open-access versions of papers, code and more are available at www.jethrobrowell.com

I'm an active member of the following organisations:


Jethro Browell's Face