We quantify the value of sub-seasonal forecasts for a real-world prediction problem: the forecasting of French month-ahead energy demand. Using surface temperature as a predictor, we construct a trading strategy and assess the financial value of using meteorological forecasts, based on actual energy demand and price data. We show that forecasts with lead times greater than two weeks can have value for this application, both on their own and in conjunction with shorter-range forecasts, especially during boreal winter. We consider a cost/loss framework based on this example, and show that, while it captures the performance of the short-range forecasts well, it misses the marginal value present in medium-range forecasts. We also contrast our assessment of forecast value to that given by traditional skill scores, which we show could be misleading if used in isolation. We emphasise the importance of basing assessment of forecast skill on variables actually used by end-users.
Electricity generation output forecasts for wind farms across Europe use numerical weather prediction (NWP) models. These forecasts influence decisions in the energy market, some of which help determine daily energy prices or the usage of thermal power generation plants. The predictive skill of power generation forecasts has an impact on the profitability of energy trading strategies and the ability to decrease carbon emissions. Probabilistic ensemble forecasts contain valuable information about the uncertainties in a forecast. The energy market typically takes basic approaches to using ensemble data to obtain more skilful forecasts. There is, however, evidence that more sophisticated approaches could yield significant further improvements in forecast skill and utility. In this letter, the application of ensemble forecasting methods to the aggregated electricity generation output for wind farms across Germany is investigated using historical ensemble forecasts from the European Centre for Medium-Range Weather Forecasting (ECMWF). Multiple methods for producing a single forecast from the ensemble are tried and tested against traditional deterministic methods. All the methods exhibit positive skill, relative to a climatological forecast, out to a lead time of at least seven days. A wind energy trading strategy involving ensemble data is implemented and produces significantly more profit than trading strategies based on single forecasts. It is thus found that ensemble spread is a good predictor for wind electricity generation output forecast uncertainty and is extremely valuable at informing wind energy trading strategy.
<p>Lake Street's aim is to help companies 'work with the weather'.&#160; Put another way, we aim to translate the user's question into scientific terms, find the best answer available, and translate it into information useful to the user in decision-making.&#160; Along the way, we have come across a host of challenges, which have encouraged public private partnerships, and inter- and transdisciplinary collaboration, to enable progress.&#160;</p><p>Using a simple example from agriculture, we will demonstrate how such interactions can enable one to see the differing perspectives of a forecast challenge.&#160; From this new perspective come new ideas, giving potential for both better numerical weather prediction models, and better crop yields.&#160; Whilst the process may seem easy given a theoretical example, the reality is that partnerships and collaborations usually come with a host of challenges, which can seem daunting.&#160;</p><p>Trust, open mindedness, acceptance that there is room for improvement are three traits that help - on both sides.&#160; We will mention some practical changes within the weather and climate research communities that we believe would lead to improved engagement with society.&#160; Meanwhile, especially as a small company, Lake Street know that collaboration enable us to both keep delivering quality products, and to feedback to the research community what questions users want answers to.</p>
<p>Lake Street's aim is to help companies 'work with the weather'.&#160; In other words, we look to deliver high value meteorological services that enable the end user to take informed action.&#160; Invariably this means processing weather data alongside datasets from the end user&#8217;s sector.&#160;</p><p>Using an example from the growing renewable energy sector, we will show how private sector companies are able to identify the relevant datasets &#8211; weather and sector specific, translate weather variables to power generation, and present a product that enables informed decision-making.&#160;</p><p>Having accurate weather information enables efficient dispatch of fossil-fuel generators when required to match demand, and so helps nations work towards net zero.&#160; This includes information about temporal and spatial correlations that are not immediately obvious from raw weather model output.&#160;</p><p>Different models are best for varying timeframes (think within day compared to next week) making no single weather model sufficient on its own.&#160; Further, the timetable of the network operator determines the cut off time for model information, and so influences model choice.</p><p>Weather forecasts are not exact and model errors can be considerable.&#160; Knowledge of uncertainties aids decision-making, yet this information is often one of the greatest challenges to calculate.&#160;&#160; &#160;&#160;</p><p>Private sector companies act as the translator, and add value at the last step, but could not do this work alone.&#160; They draw on the work of national and international bodies and academia, and in turn feedback observations about forecast error enabling model improvement.&#160; With collaboration, we increase the value of meteorological services and their usefulness to society.</p>
<p>Increasingly, operational forecasting centres are producing sub-seasonal forecasts, targeted at lead times of 3-6 weeks. These aim to fill the gap between conventional 2-week weather forecasts and longer term seasonal outlooks. However it is often difficult for end-users to know how these sub-seasonal forecasts can be best utilised, and how skilful they are for predicting variables of real world interest.</p> <p>Much prior work on sub-seasonal forecasts has focused on assessing skill scores for large-scale smooth fields of mid- or upper-tropospheric variables, or else has looked at heavily time-averaged quantities. How to extend the lessons of these studies to user applications is not always obvious.</p> <p>We take a more applied approach, focused on the chaotic and variable weather of Western Europe. We use sub-seasonal temperature forecasts alongside real-world French energy price and demand data in order to directly calculate the financial value of subseasonal forecasts to users in the energy sector. Using this new, real-world framework we make an estimate of cost-loss ratios and so can compare to the results of a simpler potential economic value model.</p> <p>&#160;</p>
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