ProbCast: Open-source Production, Evaluation and Visualisation of Probabilistic Forecasts

Browell, Jethro; Gilbert, Ciaran

doi:10.1109/pmaps47429.2020.9183441

Cited by 13 publications

(9 citation statements)

References 23 publications

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“…The market operator evaluates the scores of submitted forecasts on hourly basis and compensates accordingly. For our case study, we use an open data set from the Global Energy Forecasting Competition 2014, GEFcom2014 (Hong et al, 2016) and an open-source toolkit Prob-Cast by Browell & Gilbert (2020). The wind power measurements are normalized and thus take values in [0, 1].…”

Section: Simulation Setupmentioning

confidence: 99%

A Market for Trading Forecasts: A Wagering Mechanism

Raja¹,

Kazempour²,

Grammatico³

2022

Preprint

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The ever-increasing interest in the collection of data by advancing technical and social sectors makes it distributed in terms of ownership. Also, the diverse expertise of these owners results in the extraction of varying quality of predictive information. Thus, the platforms for pooling forecasts based on distributed data and heterogeneous predictive skills allow gaining a collective value for a prediction task. For this purpose, we design a wagering-based forecast elicitation market platform, where a buyer intending to improve their forecasts posts a prediction task, and sellers respond to it with their forecast reports and wagers. This market delivers an aggregated forecast to the buyer (pre-event) and allocates a payoff to the sellers (post-event) for their contribution. We propose a payoff mechanism and prove that it satisfies several desirable economic properties, including those specific to electronic platforms. Furthermore, we discuss the properties of the scoring rules that allow us to provide ex-ante theoretical support for showing that the quantile averaging produces higher-quality aggregate forecasts in terms of scores, compared to the widely used linear pooling methods. Finally, we provide numerical examples to illustrate the structure and properties of the proposed market platform.

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Section: Simulation Setupmentioning

confidence: 99%

A Market for Trading Forecasts: A Wagering Mechanism

Raja¹,

Kazempour²,

Grammatico³

2022

Preprint

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“…20 in Step (6). Steps (8)(9)(10)(11)(12)(13)(14) update the position of different agents by using Eq. 19 where rand SC > 0.5 while steps (17)(18)(19) update the position of different agents by using Eq.…”

Section: B Advanced Sine Cosine Algorithmmentioning

confidence: 99%

“…21 for rand SC ≤ 0.5. Steps (3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21) are repeated in the Algorithm (2) until a predefined criterion is met. The best solution P in Step 5 will be updated by exploring and exploiting the around space.…”

Section: B Advanced Sine Cosine Algorithmmentioning

confidence: 99%

“…Many authors recommend probabilistic forecasting to have better risk management. By using predictive models, visualization, and evaluation of forecasting results to produce probabilistic forecasting, a framework named ProbCast is introduced by Browell and Gilbert [8]. The ML algorithms use atmospheric variables such as wind, temperature, latitude, atmospheric pressure, etc., for nearly accurate solar radiation forecasting.…”

Section: Introductionmentioning

confidence: 99%

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Advanced Ensemble Model for Solar Radiation Forecasting Using Sine Cosine Algorithm and Newton’s Laws

et al. 2021

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As research in alternate energy sources is growing, solar radiation is catching the eyes of the research community immensely. Since solar energy generation depends on uncontrollable natural variables, without proper forecasting, this energy source cannot be trusted. For this forecasting, the use of machine learning algorithms is one of the best choices. This paper proposed an optimized solar radiation forecasting ensemble model consisting of pre-processing and training ensemble phases. The training ensemble phase works on an advanced sine cosine algorithm (ASCA) using Newton's laws of gravity and motion for objects (agents). ASCA uses sine and cosine functions to update the agent's position/velocity components by considering its mass. The training ensemble model is then developed using the k-nearest neighbors (KNN) regression. The performance of the proposed ensemble model is measured using a dataset from Kaggle (Solar Radiation Prediction, Task from NASA Hackathon). The proposed ASCA algorithm is evaluated in comparison with the Particle Swarm Optimizer (PSO), Whale Optimization Algorithm (WOA), Genetic Algorithm (GA), Grey Wolf Optimizer (GWO), Squirrel Search Algorithm (SSA), Harris Hawks Optimization (HHO), Hybrid Greedy Sine Cosine Algorithm with Differential Evolution (HGSCADE), Hybrid Modified Sine Cosine Algorithm with Cuckoo Search Algorithm (HMSCACSA), Marine Predators Algorithm (MPA), Chimp Optimization Algorithm (ChOA), and Slime Mould Algorithm (SMA). Obtained results of the proposed ensemble model are compared with those of state-of-the-art models, and significant superiority of the proposed ensemble model is confirmed using statistical analysis such as ANOVA and Wilcoxon's rank-sum tests.

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“…Jethro Browell is supported by EPSRC Fellowship (EP/R023484/1) and a visiting position at the University of Bristol as Heilbronn Visitor in Data Science in February 2020. The authors thank National Grid ESO for many discussions on forecasting and reserve setting, Graeme Hawker for support accessing GSP data, Ciaran Gilbert for contributions to Prob-Cast [38], and the anonymous editor and reviewers who helped us improve this paper. Data statement: The supplementary material attached to this paper includes data generated by this research and code to reproduce it [31].…”

Section: Acknowledgementsmentioning

confidence: 99%

Probabilistic Forecasting of Regional Net-Load With Conditional Extremes and Gridded NWP

Browell

Fasiolo

2021

IEEE Trans. Smart Grid

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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 lowprobability 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 netload, 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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ProbCast: Open-source Production, Evaluation and Visualisation of Probabilistic Forecasts

Cited by 13 publications

References 23 publications

A Market for Trading Forecasts: A Wagering Mechanism

A Market for Trading Forecasts: A Wagering Mechanism

Advanced Ensemble Model for Solar Radiation Forecasting Using Sine Cosine Algorithm and Newton’s Laws

Probabilistic Forecasting of Regional Net-Load With Conditional Extremes and Gridded NWP

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