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

Cluster-based regime-switching AR for the EEM 2017 Wind Power Forecasting Competition

This paper describes the regime-switching autoregressive models used to win the EEM 2017 Wind Power Forecasting Competition. The competition required participants to produce daily forecast wind power production for a portfolio of wind farms from 2 to 38 hours-ahead based on historic generation and numerical weather prediction analysis data only. The regimes used in the methodology presented are defined on the previous day’s weather conditions using the k-medians clustering algorithm. Cross-validation is used to identify models with the best predictive power from a pool of candidate models. The final methodology produced a final weighted mean absolute error 4.5% lower than the second place team during the two-week competition period

Use of turbine-level data for improved wind power forecasting

Short-term wind power forecasting is based on modelling the complex relationship between the weather forecasts and wind farm power production. To date, efforts to improve wind power forecasts have focused on improving Numerical Weather Prediction and wind farm power curve models. However, utility-scale wind farms cover large areas meaning that a single power curve model may struggle to represent the collective behaviour of large numbers of wind turbines. Contemporary statistical techniques are capable of processing large volumes of data, enabling the assimilation of measurements from individual wind turbines to construct a more detailed representation of wind farm power generation. Here, three state-of-the-art techniques are applied to forecast wind farm power production 1) directly from numerical weather predictions, and 2) by aggregating forecasts for individual wind turbines. Furthermore, it is observed that some wind turbines are better predictors than others and an aggregation process based on conditional weighting is proposed. In case studies of two large wind farms in the UK, it is shown that wind farm power forecasts comprising a conditional weighted sum of turbine-level predictions are superior to a direct wind farm forecast for horizons up to 48 hours ahead. Specifically, performance of the best-performing benchmark, the gradient boosting machine, is improved by 12% at Clyde South wind farm and by 6% at Gordonbush

Probabilistic access forecasting for improved offshore operations

Improving access is a priority in the offshore wind sector, driven by the opportunity to increase revenues, reduce costs, and improve safety at operational wind farms. This paper describes a novel method for producing probabilistic forecasts of safetycritical access conditions during crew transfers. Methods of generating density forecasts of significant wave height and peak wave period are developed and evaluated. It is found that boosted semi-parametric models outperform those estimated via maximum likelihood, as well as a non-parametric approach. Scenario forecasts of sea-state variables are generated and used as inputs to a datadriven vessel motion model, based on telemetry recorded during 700 crew transfers. This enables the production of probabilistic access forecasts of vessel motion during crew transfer up to 5 days ahead. The above methodology is implemented on a case study at a wind farm off the east coast of the UK

Quantile combination for the EEM Wind Power Forecasting Competition

Combining forecasts is an established strategy for improving predictions and is employed here to produce probabilistic forecasts of regional wind power production in Sweden, finishing in second place in the EEM20 Wind Power Forecasting Competition. We combine quantile forecasts from two models with different characteristics: a ‘discrete’ tree-based model and ‘smooth’ generalised additive model. Quantiles are combined via linear weighting and the resulting combination is superior than both constituent forecasts in all four regions considered

Dirichlet sampled capacity and loss estimation for LV distribution networks with partial observability

With low voltage (LV) distribution networks increasingly being re-purposed beyond their original design specifications to accommodate low carbon technologies, the ability to accurately calculate their actual spare capacity is critical. Traditionally, within the Great Britain (GB) power system, there has been limited monitoring of LV distribution networks, making this difficult. This paper proposes a method for estimating spare capacity of unmonitored LV networks using demand data from customer Smart Meters. In particular, the proposed method infers existing LV network capacity, as well as losses, across scenarios where only a limited number of customers have Smart Meters installed. Typical daily load profiles across customers with Smart Meters are learned using a Dirichlet sampled Gaussian mixture model (GMM). Learned profiles are then applied to all unmetered customers to estimate network parameters. Method accuracy is assessed by comparing estimations with simulated, fully observed, LV network models. The method is also compared to benchmark models for establishing unobserved demand profiles. Overall, results in the paper show that the proposed method outperforms benchmark models in terms of accurately assessing substation headroom, particularly in scenarios where only 10-50% of customers have Smart Meters installed

Power available signals, zero-carbon ESO and new revenue streams

Contents • Power Available – What is a power available signal? – What is it for? • Wind Providing Frequency Response – Today and in the future… • PA accuracy and proposed standar

A hierarchical approach to probabilistic wind power forecasting

This paper describes a method to generate improved probabilistic wind farm power forecasts in a hierarchical framework with the incorporation of production data from individual wind turbines. Wind power forms a natural hierarchy as generated electricity is aggregated from the individual turbine, to farm, to the regional level and so on. To forecast the wind farm power generation, a layered approach is proposed whereby deterministic forecasts from the lower layer (turbine level) are used as input features to an upper-level (wind farm) probabilistic model. In a case study at a utility scale wind farm it is shown that improvements in probabilistic forecast skill (CRPS) of 1.24% and 2.39% are obtainable when compared to two very competitive benchmarks based on direct forecasting of the wind farm power using Gradient Boosting Trees and an Analog Ensemble, respectively

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

Short-term forecasts of energy consumption are invaluable for 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%

Subseasonal-to-seasonal forecasting for wind turbine maintenance scheduling

Certain wind turbine maintenance tasks require specialist equipment, such as a large crane for heavy lift operations. Equipment hire often has a lead time of several weeks but equipment use is restricted by future weather conditions through wind speed safety limits, necessitating an assessment of future weather conditions. This paper sets out a methodology for producing subseasonal-to-seasonal (up to 6 weeks ahead) forecasts that are site- and task-specific. Forecasts are shown to improve on climatology at all sites, with fair skill out to six weeks for both variability and weather window forecasts. For the case of crane hire, a cost-loss model identifies the range of electricity prices where the hiring decision is sensitive to the forecasts. While there is little difference in the hiring decision made by the proposed forecasts and the climatology benchmark at most electricity prices, the repair cost per turbine is reduced at lower electricity prices