arXiv subjects

Jethro Browell

Publications and source records attributed to Jethro Browell.

14 recordsLinked to original sources

arXiv2026

Risk-Based Dynamic Thermal Rating in Distribution Transformers via Probabilistic Forecasting

Low voltage (LV) distribution transformers face accelerating demand growth while replacement lead times and costs continue to rise, making improved utilisation of existing assets essential. Static and conservative protection devices (PDs) in distribution transformers are inflexible and limit the available headroom of the transformer. This paper presents a probabilistic framework for dynamically forecasting optimal thermal protection settings. The proposed approach directly predicts the day-ahead scale factor which maximises the dynamic thermal rating of the transformer from historical load, temperature, and metadata using clustered quantile regression models trained on 644 UK LV transformers. Probabilistic forecasting quantifies overheating risk directly through the prediction percentile, enabling risk-informed operational decisions. Results show a 10--12\% additional capacity gain compared to static settings, with hotspot temperature risk matching the selected percentile, including under realistic temperature forecast errors. These results demonstrate a practical approach for distribution network operators to take advantage of PDs with adaptive settings to maximise capacity and manage risk on operational time scales.

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arXiv2026

Probabilistic forecasting of weather-driven faults in electricity networks: a flexible approach for extreme and non-extreme events

Electricity networks are vulnerable to weather damage, with severe events often leading to faults and power outages. Timely forecasts of fault occurrences, ranging from nowcasts to several days ahead, can enhance preparedness, support faster response, and reduce outage durations. To be operationally useful, such forecasts must quantify uncertainty, enabling risk-informed resource allocation. We present a novel probabilistic framework for forecasting fault counts that captures typical and extreme events. Non-extreme faults are modeled linearly interpolating estimates from multiple additive quantile regressions, while extreme events are described through a discrete generalized Pareto distribution. To incorporate the impact of weather fluctuations, we use ensemble numerical weather predictions, which helps to quantify uncertainty in the forecasts. This approach is designed to provide reliable fault predictions up to four days ahead. We evaluate the model through numerical experiments and apply it to historical fault data from two electricity distribution networks in Great Britain. The resulting forecasts demonstrate substantial improvements over business-as-usual and alternative modeling approaches. A practitioner trial conducted with Scottish Power Energy Networks from October 2024 to March 2025 further demonstrates the operational value of the forecasts. Engineers found them sufficiently reliable to inform decision-making, offering benefits to both network operators and electricity consumers.

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arXiv2025

The Hybrid Renewable Energy Forecasting and Trading Competition 2024

The Hybrid Energy Forecasting and Trading Competition challenged participants to forecast and trade the electricity generation from a 3.6GW portfolio of wind and solar farms in Great Britain for three months in 2024. The competition mimicked operational practice with participants required to submit genuine forecasts and market bids for the day-ahead on a daily basis. Prizes were awarded for forecasting performance measured by Pinball Score, trading performance measured by total revenue, and combined performance based on rank in the other two tracks. Here we present an analysis of the participants' performance and the learnings from the competition. The forecasting track reaffirms the competitiveness of popular gradient boosted tree algorithms for day-ahead wind and solar power forecasting, though other methods also yielded strong results, with performance in all cases highly dependent on implementation. The trading track offers insight into the relationship between forecast skill and value, with trading strategy and underlying forecasts influencing performance. All competition data, including power production, weather forecasts, electricity market data, and participants' submissions are shared for further analysis and benchmarking.

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arXiv2025

Adaptive Bayesian Very Short-Term Wind Power Forecasting Based on the Generalised Logit Transformation

Wind power plays an increasingly significant role in achieving the 2050 Net Zero Strategy. Despite its rapid growth, its inherent variability presents challenges in forecasting. Accurately forecasting wind power generation is one key demand for the stable and controllable integration of renewable energy into existing grid operations. This paper proposes an adaptive method for very short-term forecasting that combines the generalised logit transformation with a Bayesian approach. The generalised logit transformation processes double-bounded wind power data to an unbounded domain, facilitating the application of Bayesian methods. A novel adaptive mechanism for updating the transformation shape parameter is introduced to leverage Bayesian updates by recovering a small sample of representative data. Four adaptive forecasting methods are investigated, evaluating their advantages and limitations through an extensive case study of over 100 wind farms ranging four years in the UK. The methods are evaluated using the Continuous Ranked Probability Score and we propose the use of functional reliability diagrams to assess calibration. Results indicate that the proposed Bayesian method with adaptive shape parameter updating outperforms benchmarks, yielding consistent improvements in CRPS and forecast reliability. The method effectively addresses uncertainty, ensuring robust and accurate probabilistic forecasting which is essential for grid integration and decision-making.

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arXiv2025

evalprob4cast: An R-package for evaluation of ensembles as probabilistic forecasts or event forecasts

For any forecasting application, evaluation of forecasts is an important task. For example, in the field of renewable energy sources there is high variability and uncertainty of power production, which makes forecasting and the evaluation hereof crucial both for power trading and power grid balancing. In particular, probabilistic forecasts represented by ensembles are popular due to their ability to cover the full range of scenarios that can occur, thus enabling forecast users to make more informed decisions than what would be possible with simple deterministic forecasts. The selection of open source software that supports evaluation of ensemble forecasts, and especially event detection, is currently limited. As a solution, evalprob4cast is a new R-package for probabilistic forecast evaluation that aims to provide its users with all the tools needed for the assessment of ensemble forecasts, in the form of metrics and visualization methods. Both univariate and multivariate probabilistic forecasts as well as event detection are covered. Furthermore, it offers a user-friendly design where all of the evaluation methods can be applied in a fast and easy way, as long as the input data is organized in accordance with the format defined by the package. While its development is motivated by forecasting of renewables, the package can be used for any application with ensemble forecasts.

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arXiv2025

Seamless short- to mid-term probabilistic wind power forecasting

This paper presents a method for probabilistic wind power forecasting that quantifies and integrates uncertainties from weather forecasts and weather-to-power conversion. By addressing both uncertainty sources, the method achieves state-of-the-art results for lead times of 6 to 162 hours, eliminating the need for separate models for short- and mid-term forecasting. It also improves short-term forecasts during high weather uncertainty periods, which methods based on deterministic weather forecasts fail to capture. The study reveals that weather-to-power uncertainty is more significant for short-term forecasts, while weather forecast uncertainty dominates mid-term forecasts, with the transition point varying between wind farms. Offshore farms typically see this shift at shorter lead times than onshore ones. The findings are supported by an extensive, reproducible case study comprising 73 wind farms in Great Britain over five years.

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arXiv2023

Overcoming the disconnect between energy system and climate modeling

Energy system models underpin decisions by energy system planners and operators. Energy system modelling faces a transformation: accounting for changing meteorological conditions imposed by climate change. To enable that transformation, a community of practice in energy-climate modelling has started to form that aims to better integrate energy system models with weather and climate models. Here, we evaluate the disconnects between the energy system and climate modelling communities, then lay out a research agenda to bridge those disconnects. In the near-term, we propose interdisciplinary activities for expediting uptake of future climate data in energy system modelling. In the long-term, we propose a transdisciplinary approach to enable development of (1) energy-system-tailored climate datasets for historical and future meteorological conditions and (2) energy system models that can effectively leverage those datasets. This agenda increases the odds of meeting ambitious climate mitigation goals by systematically capturing and mitigating climate risk in energy sector decision making.

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arXiv2023

Adaptive Probabilistic Forecasting of Electricity (Net-)Load

Electricity load forecasting is a necessary capability for power system operators and electricity market participants. The proliferation of local generation, demand response, and electrification of heat and transport are changing the fundamental drivers of electricity load and increasing the complexity of load modelling and forecasting. We address this challenge in two ways. First, our setting is adaptive; our models take into account the most recent observations available, yielding a forecasting strategy able to automatically respond to changes in the underlying process. Second, we consider probabilistic rather than point forecasting; indeed, uncertainty quantification is required to operate electricity systems efficiently and reliably. Our methodology relies on the Kalman filter, previously used successfully for adaptive point load forecasting. The probabilistic forecasts are obtained by quantile regressions on the residuals of the point forecasting model. We achieve adaptive quantile regressions using the online gradient descent; we avoid the choice of the gradient step size considering multiple learning rates and aggregation of experts. We apply the method to two data sets: the regional net-load in Great Britain and the demand of seven large cities in the United States. Adaptive procedures improve forecast performance substantially in both use cases for both point and probabilistic forecasting.

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arXiv2023

Probabilistic load forecasting for the low voltage network: forecast fusion and daily peaks

Short-term forecasts of energy consumption are invaluable for the operation of energy systems, including low voltage electricity networks. However, network loads are challenging to predict when highly desegregated to small numbers of customers, which may be dominated by individual behaviours rather than the smooth profiles associated with aggregate consumption. Furthermore, distribution networks are challenged almost entirely by peak loads, and tasks such as scheduling storage and/or demand flexibility maybe be driven by predicted peak demand, a feature that is often poorly characterised by general-purpose forecasting methods. Here we propose an approach to predict the timing and level of daily peak demand, and a data fusion procedure for combining conventional and peak forecasts to produce a general-purpose probabilistic forecast with improved performance during peaks. The proposed approach is demonstrated using real smart meter data and a hypothetical low voltage network hierarchy comprising feeders, secondary and primary substations. Fusing state-of-the-art probabilistic load forecasts with peak forecasts is found to improve performance overall, particularly at smart-meter and feeder levels and during peak hours, where improvement in terms of CRPS exceeds 10%.

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arXiv2022

Forecasting: theory and practice

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases.

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arXiv2021

The application of sub-seasonal to seasonal (S2S) predictions for hydropower forecasting

Inflow forecasts play an essential role in the management of hydropower reservoirs. Forecasts help operators schedule power generation in advance to maximise economic value, mitigate downstream flood risk, and meet environmental requirements. The horizon of operational inflow forecasts is often limited in range to ~2 weeks ahead, marking the predictability barrier of deterministic weather forecasts. Reliable inflow forecasts in the sub-seasonal to seasonal (S2S) range would allow operators to take proactive action to mitigate risks of adverse weather conditions, thereby improving water management and increasing revenue. This study outlines a method of deriving skilful S2S inflow forecasts using a case study reservoir in the Scottish Highlands. We generate ensemble inflow forecasts by training a linear regression model for the observed inflow onto S2S ensemble precipitation predictions from the European Centre for Medium-range Weather Forecasting (ECMWF). Subsequently, post-processing techniques from Ensemble Model Output Statistics are applied to derive calibrated S2S probabilistic inflow forecasts, without the application of a separate hydrological model. We find the S2S probabilistic inflow forecasts hold skill relative to climatological forecasts up to 6 weeks ahead. The inflow forecasts hold greater skill during winter compared with summer. The forecasts, however, struggle to predict high summer inflows, even at short lead-times. The potential for the S2S probabilistic inflow forecasts to improve water management and deliver increased economic value is confirmed using a stylised cost model. While applied to hydropower forecasting, the results and methods presented here are relevant to broader fields of water management and S2S forecasting applications.

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arXiv2021

Probabilistic Forecasting of Regional Net-load with Conditional Extremes and Gridded NWP

The increasing penetration of embedded renewables makes forecasting net-load, consumption less embedded generation, a significant and growing challenge. Here a framework for producing probabilistic forecasts of net-load is proposed with particular attention given to the tails of predictive distributions, which are required for managing risk associated with low-probability events. Only small volumes of data are available in the tails, by definition, so estimation of predictive models and forecast evaluation requires special attention. We propose a solution based on a best-in-class load forecasting methodology adapted for net-load, and model the tails of predictive distributions with the Generalised Pareto Distribution, allowing its parameters to vary smoothly as functions of covariates. The resulting forecasts are shown to be calibrated and sharper than those produced with unconditional tail distributions. In a use-case inspired evaluation exercise based on reserve setting, the conditional tails are shown to reduce the overall volume of reserve required to manage a given risk. Furthermore, they identify periods of high risk not captured by other methods. The proposed method therefore enables user to both reduce costs and avoid excess risk.

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arXiv2017

Risk Constrained Trading Strategies for Stochastic Generation with a Single-Price Balancing Market

Due to the limited predictability of wind power and other stochastic generation, trading this energy in competitive electricity markets is challenging. This paper derives revenue-maximising and risk-constrained strategies for stochastic generators participating in electricity markets with a single-price balancing mechanism. Starting from the optimal---and impractical---strategy of offering zero or nominal power, which exposes the participant to potentially large imbalance costs, we develop a number of strategies that control risk by hedging against penalising balancing prices in favour of rewarding ones. Trading strategies are formulated in a probabilistic framework in order to address asymmetry in balancing prices. The large-scale communication of system information characteristic of modern power systems is utilised to inputs for electricity price forecasts and probabilistic system length forecasts. A case study using data from the GB market in the UK is presented and the ability of the proposed strategies to increase revenue and reduce risk is demonstrated and analysed.

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arXiv2017

Improved Very-short-term Wind Forecasting using Atmospheric Classification

We present a regime-switching vector-autoregressive method for very-short-term wind speed forecasting at multiple locations with regimes based on large-scale meteorological phenomena. Statistical methods short-term wind forecasting out-perform numerical weather prediction for forecast horizons up to a few hours, and the spatio-temporal interdependency between geographically dispersed locations may be exploited to improve forecast skill. Here we show that conditioning spatio-temporal interdependency on `atmospheric modes' can further improve forecast performance. The modes are defined from the atmospheric classification of wind and pressure fields at the surface level, and the geopotential height field at the 500hPa level. The data fields are extracted from the MERRA-2 reanalysis dataset with an hourly temporal resolution over the UK, atmospheric patterns are classified using self-organising maps and then clustered to optimise forecast performance. In a case study based on 6 years of measurements from 23 weather stations in the UK, a set of three atmospheric modes are found to be optimal for forecast performance. The skill in the one- to six-hour-ahead forecasts is improved at all sites compared to persistence and competitive benchmarks. Across the 23 test sites, one-hour-ahead root mean squared error is reduced by between 0.3% and 4.1% compared to the best performing benchmark, and by and average of 1.6% over all sites; the six-hour-ahead accuracy is improved by an average of 3.1%.

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