Publications
Most of my published work is open-access. Please contact me if you have any issues accessing something you’d like to read. You can download this list in BibTeX here.
Working Papers
T. Shen, J. Browell and D. Castro-Camilo. Adaptive Bayesian Very Short-Term Wind Power Forecasting Based on the Generalised Logit Transformation. Submitted [arXiv]
K. Stylpnopoulos, J. Browell, J. Illian, C. Gilbert and W. Jones. Intra-day imbalance price forecasting leveraging continuous trade data. Submitted
P. Ghelasi, J. Browell and F. Ziel. A probabilistic merit order model for day-ahead electricity price forecasting. Working paper
J. Linley, F. Tough, V. Davies and J. Browell. Quantifying Residential Financed Emissions: Evaluating Data Scenarios under the PCAF Framework. Working paper
Published Work
2026
S. Angus, J. Browell, D. Greenwood and M. Deakin. Risk-Based Dynamic Thermal Rating in Distribution Transformers via Probabilistic Forecasting. Electric Power Systems Research, Accepted [arXiv]
M. Maia, D. Castro-Camilo and J. Browell. Probabilistic forecasting of weather-driven faults in electricity networks: a flexible approach for extreme and non-extreme events. Technometrics, 0(ja), 1–19 [doi]
G. Dantas and J. Browell. Seamless Short- to Mid-Term Probabilistic Wind Power Forecasting. Wind Energy, 29(2), e70079 [doi]
2025
- J. Browell, D. van der Meer, H. Kälvegren, S. Haglund, E. Simioni, et al.. The hybrid renewable energy forecasting and trading competition 2024. International Journal of Forecasting [doi]
2024
M. Schneider, J. Browell and R. Rankin. Limiting extreme behavior in forecasting competitions. Foresight, 75, 32-37 [link]
V. Gioia, M. Fasiolo, J. Browell and R. Bellio. Additive Covariance Matrix Models: Modeling Regional Electricity Net-Demand in Great Britain. Journal of the American Statistical Association, 120(549), 107–119 [doi]
J. de Vilmarest, J. Browell, M. Fasiolo, Y. Goude and O. Wintenberger. Adaptive Probabilistic Forecasting of Electricity (Net-)Load. IEEE Transactions on Power Systems, 39(2), 4154–4163 [doi]
2023
D. L. Donaldson, J. Browell and C. Gilbert. Predicting the magnitude and timing of peak electricity demand: A competition case study. IET Smart Grid, 7(4), 473–484 [doi]
C. Gilbert, J. Browell and B. Stephen. Probabilistic load forecasting for the low voltage network: Forecast fusion and daily peaks. Sustainable Energy, Grids and Networks, 34, 100998 [doi]
M. Hu, B. Stephen, J. Browell, S. Haben and D. C. Wallom. 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]
2022
J. Browell, C. Gilbert and M. Fasiolo. Covariance structures for high-dimensional energy forecasting. Electric Power Systems Research, 211, 108446 [doi]
E. Heylen, J. Browell and F. Teng. Probabilistic Day-Ahead Inertia Forecasting. IEEE Transactions on Power Systems, 37(5), 3738–3746 [doi]
D. Huppmann, J. Browell, B. Nastasi, Z. Vale and D. Süsser. A research agenda for open energy science: Opportunities and perspectives of the F1000Research Energy Gateway. F1000Research, 11, 896 [doi]
M. T. Craig, J. Wohland, L. P. Stoop, A. Kies, B. Pickering, et al.. Overcoming the disconnect between energy system and climate modeling. Joule, 6(7), 1405–1417 [doi]
J. Browell and C. Gilbert. Predicting electricity imbalance prices and volumes: capabilities and opportunities. Energies, 15(10) [doi]
R. Tawn, J. Browell and D. McMillan. Subseasonal-to-Seasonal Forecasting for Wind Turbine Maintenance Scheduling. Wind, 2(2), 260–287 [doi]
R. M. Graham, J. Browell, D. Bertram and C. J. White. The application of sub-seasonal to seasonal (S2S) predictions for hydropower forecasting. Meteorological Applications, 29(1) [doi]
R. Tawn and J. Browell. A review of very short-term wind and solar power forecasting. Renewable and Sustainable Energy Reviews, 153, 111758 [doi]
M. Farrokhabadi, J. Browell, Y. Wang, S. Makonin, W. Su, et al.. Day-Ahead Electricity Demand Forecasting Competition: Post-COVID Paradigm. IEEE Open Access Journal of Power and Energy, 9, 185–191 [doi]
2021
C. J. White, D. I. V. Domeisen, N. Acharya, E. A. Adefisan, M. L. Anderson, et al.. Advances in the application and utility of subseasonal-to-seasonal predictions. Bulletin of the American Meteorological Society [doi]
R. Telford, B. Stephen, J. Browell and S. Haben. Dirichlet sampled capacity and loss estimation for LV distribution networks with partial observability. IEEE Transactions on Power Delivery, 36(5), 2676-2686 [doi]
E. Medina-Lopez, D. McMillan, J. Lazic, E. Hart, S. Zen, et al.. Satellite data for the offshore renewable energy sector: Synergies and innovation opportunities. Remote Sensing of Environment, 264, 112588 [doi]
C. Gilbert, J. Browell and D. McMillan. Probabilistic access forecasting for improved offshore operations. International Journal of Forecasting, 37(1), 134–150 [doi]
H. Bloomfield, P. Gonzalez, J. Lundquist, L. Stoop, J. Browell, et al.. The importance of weather and climate to energy systems: a workshop on Next Generation Challenges in Energy-Climate Modelling. Bulletin of the American Meteorological Society, 102(1), E159–E167 [doi]
J. Browell and M. Fasiolo. Probabilistic Forecasting of Regional Net-load with Conditional Extremes and Gridded NWP. IEEE Transactions on Smart Grid, 12, 5011 - 5019 [doi]
2020
R. Tawn, J. Browell and I. Dinwoodie. Missing data in wind farm time series: Properties and effect on forecasts. Electric Power Systems Research, 189, 106640 [doi]
C. Gilbert, J. Browell and D. McMillan. Leveraging turbine-level data for improved probabilistic wind power forecasting. IEEE Transactions on Sustainable Energy, 11(3), 1152–1160 [doi]
J. W. Messner, P. Pinson, J. Browell, M. B. Bjerregand I. Schicker. Evaluation of wind power forecasts – an up-to-date view. Wind Energy, 23(6), 1461–1481 [doi]
M. Nedd, J. Browell, K. Bell and C. Booth. Containing a credible loss to within frequency stability limits in a low inertia GB power system. IEEE Transactions on Industry Applications, 56(2), 1031–1039 [doi]
2019
C. Edmunds, S. Martín-Martínez, J. Browell, E. Gómez-Lázaro and S. Galloway. On the participation of wind energy in response and reserve markets in Great Britain and Spain. Renewable and Sustainable Energy Reviews, 115, 109360 [doi]
C. Sweeney, R. J. Bessa, J. Browell and P. Pinson. The future of forecasting for renewable energy. WIREs: Energy and Environment, 9(2) [doi]
2018
J. Browell, D. R. Drew and K. Philippopoulos. Improved very short-term spatio-temporal wind forecasting using atmospheric regimes. Wind Energy, 21(11), 968-979 [doi]
J. Browell. Risk Constrained Trading Strategies for Stochastic Generation with a Single-Price Balancing Market. Energies, 11(6), 1345 [doi]
2017
R. J. Bessa, C. Möhrlen, V. Fundel, M. Siefert, J. Browell, et al.. Towards improved understanding of the applicability of uncertainty forecasts in the electric power industry. Energies, 10(9) [doi]
L. Cavalcante, R. J. Bessa, M. Reis and J. Browell. LASSO vector autoregression structures for very short-term wind power forecasting. Wind Energy, 20(4), 657–675 [doi]
A. Malvaldi, S. Weiss, D. Infield, J. Browell, P. Leahy, et al.. A spatial and temporal correlation analysis of aggregate wind power in an ideally interconnected Europe. Wind Energy, 20(8), 1315-1329 [doi]
J. Browell, C. Gilbert and D. McMillan. Use of turbine-level data for improved wind power forecasting. IEEE Manchester PowerTech, Powertech 2017
2016
J. Dowell and P. Pinson. Very-short-term probabilistic wind power forecasts by sparse vector autoregression. IEEE Transactions on Smart Grid, 7(2), 763–770 [doi]
V. Catterson, D. McMillan, I. Dinwoodie, M. Revie, J. Dowell, et al.. An economic impact metric for evaluating wave height forecasters for offshore wind maintenance access. Wind Energy, 19(2), 199–212 [doi]
2014
J. Dowell, S. Weiss, D. Hill and D. Infield. Short-term spatio-temporal prediction of wind speed and direction. Wind Energy, 17(12), 1945–1955 [doi]
J. Dowell, A. Zitrou, L. Walls, T. Bedford and D. Infield. Analysis of wind and wave data to assess maintenance access to offshore wind farms. Safety, Reliability and Risk Analysis: Beyond the Horizon - Proceedings of the European Safety and Reliability Conference, ESREL 2013, 743-750
2025
- G. Giebel, C. Draxl, C. Möhrlen, H. Frank, J. Zack, et al.. Extreme Power System Events, Results from a Recent Workshop of IEA Wind Task 51 Forecasting. In Proceedings of 2025 Wind Energy Science Conference [link]
2020
J. Browell, C. Gilbert, R. Tawn and L. May. Quantile combination for the EEM Wind Power Forecasting Competition. In 2020 17th International Conference on the European Energy Market (EEM) [doi]
J. Browell and C. Gilbert. ProbCast: Open-source production, evaluation and visualisation of probabilistic forecasts. In 2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS) [doi]
2019
C. Gilbert, J. Browell and D. McMillan. A data-driven vessel motion model for offshore access forecasting. In OCEANS 2019 - Marseille [doi]
J. Browell, C. Möhrlen, J. Zack and J. W. Messner. IEA wind recommended practices for selecting renewable power forecasting solutions: part 3 : evaluation of forecasts and forecast solutions. [link]
2018
C. Möhrlen, J. Lerner, J. W. Messner, J. Browell, A. Tuohy, et al.. 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. [link]
C. Gilbert, J. Browell and D. McMillan. A hierarchical approach to probabilistic wind power forecasting. In 2018 IEEE International Conference on Probabilistic Methods Applied to Power Systems (PMAPS) [doi]
2017
- J. Browell and C. Gilbert. Cluster-based regime-switching AR for the EEM 2017 Wind Power Forecasting Competition. In 2017 14th International Conference on the European Energy Market (EEM) [doi]
2016
A. Malvaldi, J. Dowell, S. Weiss and D. Infield. Wind prediction enhancement by exploiting data non-stationarity. In 2nd IET International Conference on Intelligent Signal Processing 2015 (ISP) [doi]
J. Dowell, G. Hawker, K. Bell and S. Gill. A review of probabilistic methods for defining reserve requirements. In 2016 IEEE Power and Energy Society General Meeting [doi]
J. Browell, I. Dinwoodie and D. McMillan. Forecasting for day-ahead offshore maintenance scheduling under uncertainty. In Proceedings of the European Safety and Reliability (ESREL) Conference, 2016 [link]
2015
- J. Dowell, S. Weiss and D. Infield. Kernel methods for short-term spatio-temporal wind prediction. In 2015 IEEE Power and Energy Society General Meeting [doi]
2014
A. Malvaldi, J. Dowell, S. Weiss, D. Infield and D. Hill. Wind prediction enhancement by supplementing measurements with numerical weather prediction now-casts. 1–4 [link]
J. Dowell, S. Weiss, D. Infield and S. Chandna. A widely linear multichannel Wiener filter for wind prediction. 29–32 [doi]
J. Dowell, S. Weiss and D. Infield. Spatio-temporal prediction of wind speed and direction by continuous directional regime. 1–5 [doi]
2013
J. Dowell and S. Weiss. Short-term wind prediction using an ensemble of particle swarm optimised FIR filters. In IET Intelligent Signal Processing Conference 2013 (ISP 2013) [doi]
J. Dowell, S. Weiss, D. Hill and D. Infield. A cyclo-stationary complex multichannel wiener filter for the prediction of wind speed and direction.
J. Dowell, L. Walls, A. Zitrou and D. Infield. Analysis of wind and wave data to assess maintenance access for offshore wind farms.
J. Dowell, S. Weiss, D. Hill and D. Infield. Improved spatial modelling of wind fields. European Wind Energy Association Conference
2022
- C. Möhrlen, J. Zack and G. Giebel. IEA Wind Recommended Practice for the Implementation of Renewable Energy Forecasting Solutions. International Energy Agency with contributions from Jethro Browell and others. [doi]
2020
- M. Nedd, J. Browell, A. Egea-Alvarez, K. Bell, R. Hamilton, et al.. Operating a zero-carbon GB power system: implications for Scotland. ClimateXChange [doi]
2019
- J. Browell, A. Stock and D. McMillan. Recommendation for the Evaluation of Wind Farm Power Available Signal Accuracy. University of Strathclyde
2017
- D. McMillan and J. Browell. Optimisation of Wind Energy O&M Decision Making Under Uncertainty [Final Report]: Exploitation Plan. University of Strathclyde
2016
- R. J. Bessa, J. Dowell and P. Pinson. Renewable energy forecasting. In Smart Grid Handbook, pp. 639–659, John Wiley & Sons, Ltd. [doi]
Note: I married Naomi Brown in 2016 and we combined our surnames, Brown and Dowell, to create “Browell”.