How to perform a benchmark of renewable energy forecasting solutions?
Recommended practices for the selection of the best solar and wind forecasting provider.
Company news
18/04/2024
by
Etienne Nardeau
15 min
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The production of wind and solar energy is inherently tied to weather conditions, making it unpredictable and intermittently disruptive to the stability of the power grid. Forecasting the production of energy has become essential for maintaining grid balance or trade electricity, and facilitating the seamless integration of variable renewable energy sources into the electricity mix.
The challenge lies in obtaining the most effective forecast data to enhance decision-making tools. In this context, benchmarking serves as a powerful instrument, allowing for the evaluation and comparison of the performance of different weather and generation forecasting providers to identify the most accurate ones.
We participate in several benchmarking exercises each year, and often the organizations doing the benchmark ask us for recommendations for implementation. In response to this request, this article provides a list of best practices, from the International Energy Agency [1], for conducting a benchmark in a way that ensures the highest levels of benefit and efficiency.
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1. Set a distinct goal
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It is crucial to specify the exact aim of the benchmarking process. This involves determining what is hoped to be achieved by comparing different entities, whether it's improving efficiency, enhancing performance, or identifying best practices. A well-defined objective guides the entire benchmarking exercise for solar and/or wind forecasts, ensuring it remains focused and relevant.
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How to perform a benchmark of renewable energy forecasting solutions?
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2. Establish precise metrics and evaluation criteria
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Before initiating the benchmarking process, it is essential to carefully select and define the specific metrics and criteria that will be used for assessing performance. These metrics should be relevant to the objectives of the benchmark and capable of providing clear, measurable indicators. For instance, metrics typically used for day-ahead forecast verification may not be relevant for very-short term forecast evaluation. By setting these parameters in advance, you create a transparent and objective foundation upon which to compare different solutions, ensuring that the selection process is both fair and informed.
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3. Formulate a detailed schedule
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Crafting a comprehensive timeline is a critical step in the benchmarking process of renewable energy forecasting solutions. This timeline should clearly delineate the start and end dates of each phase of the process, including when selections will be announced and when contracts will be awarded. By doing so, you establish a structured framework that outlines not only the duration of each key milestone, but also the sequence in which they will occur. This level of detail ensures that all parties involved have a clear understanding of the timeline, contributing to a more organized and efficient process. Additionally, setting explicit deadlines helps in maintaining momentum and focus throughout the benchmarking exercise, facilitating timely decision-making and minimizing the likelihood of delays.
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4. Prioritize confidentiality in the dissemination of results
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It is important to initiate discussions with forecast service providers to gain their consent for sharing forecast results in an anonymized format. This step is crucial for creating a setting that encourages ongoing improvement and fosters a culture of self-reflection about the accuracy of forecasts among providers. By removing identifiable information, service providers may feel more comfortable participating in the benchmarking process, knowing that their data will be used constructively without exposing proprietary details. Anonymizing results also allows for an open exchange of performance insights, encouraging providers to learn from the aggregated data and identify opportunities for enhancing their forecasting models.
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5. Implement a preparatory question-and-answer phase
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Establishing a designated period of 1–2 weeks for questions and answers prior to the start of the benchmarking process is a strategic move to enhance the readiness and comprehension of all participants. This phase serves as a vital opportunity for stakeholders, including forecast service providers and benchmark organizers, to clarify any uncertainties, address specific concerns, and provide detailed explanations about the methodology, objectives, and expectations of the benchmark. By dedicating time to this interactive exchange, participants can fully understand the scope and requirements of the benchmarking exercise, ensuring that everyone is on the same page from the outset. This preparatory step not only aids in mitigating potential misunderstandings or confusion during the actual benchmarking period but also fosters a more collaborative and transparent environment. The ultimate goal is to equip all involved parties with the necessary information and confidence to engage in the benchmarking process effectively, contributing to its overall success and the quality of the insights gained.
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6. Assign sufficient duration for evaluating data exchange processes
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It is critical to allocate an appropriate amount of time specifically for testing the mechanisms involved in transferring data between the participants and the benchmark operators before the actual benchmarking activity commences. This testing phase is designed to ensure that the data exchange is both efficient and secure, addressing any technical issues, such as compatibility problems or data integrity concerns, that might impede the smooth flow of information. By dedicating time to thoroughly review and adjust the data transfer protocols as needed, participants can be confident that their contributions will be accurately represented and that the benchmarking process will not be hindered by preventable logistical setbacks. This step not only facilitates a more effective collaboration by smoothing out any potential technical obstacles but also reinforces the reliability and credibility of the benchmarking exercise itself. Ensuring that the data transfer system is robust and tested in advance lays the groundwork for a successful benchmarking initiative, where insights and findings are derived from complete and untainted data sets.
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7. Uphold efficient and immediate dialogue
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It is essential to establish a communication framework that guarantees swift and direct interactions with all individuals involved in the benchmarking process. This entails promptly informing participants about any modifications to the benchmark’s structure or the procedures being followed, as well as quickly responding to any inquiries they may present. Implementing a proactive communication strategy ensures that all stakeholders are kept in the loop and can adjust their preparations or expectations accordingly. Such an approach minimizes confusion and fosters a transparent environment, where participants feel valued and informed. By prioritizing clear and quick communication channels, it becomes possible to preemptively address potential issues, facilitate smoother operations, and reinforce the collaborative nature of the benchmark.
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8. Ensure uniform forecast file submissions
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Mandating a standardized format for forecast files from all participants is a crucial step in streamlining the benchmarking process. To achieve this uniformity, it's advisable to not only request that submissions adhere to a specific format, but also to supply an exemplar file as a clear guide. This approach significantly reduces discrepancies in data presentation, making it easier to compare, analyze, and interpret the information provided by different solar and wind forecast service providers. By setting a common standard and offering a model for how the forecast data should be structured, you facilitate a smoother integration of data from diverse sources. This uniformity in submission helps to prevent potential delays and complications that might arise from having to reformat or clarify data submissions, thereby enhancing the efficiency of the benchmarking process. Moreover, it ensures that all participants are on equal footing, with a clear understanding of the expectations, which promotes fairness and objectivity in the evaluation of forecast accuracy and performance.
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9. Aim for standardized data presentations
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Achieving consistency in the formats of both observational and forecast data files is paramount for the smooth operation of a benchmarking process. It is advisable to work towards ensuring that all data submissions align with the specific requirements and preferences of the benchmark operator, particularly once a contract is in place. This means establishing clear guidelines for how data should be structured, formatted, and delivered by all participating entities. By setting these standards early on and communicating them effectively, you can greatly reduce the potential for misalignment and the need for time-consuming data reformatting or clarification later in the process. Ultimately, uniform data formats help ensure that the benchmarking exercise is focused on accurately assessing the performance of the forecasts themselves, rather than being encumbered by inconsistencies in data presentation.
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10. Ensure equitable access to background information
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It is critical to supply every participant in the benchmarking process with the same set of historical data and project metadata. This approach is fundamental in creating an equitable environment where each participant is evaluated under identical conditions. By distributing uniform historical data, such as past weather patterns, energy production records, or other relevant benchmarks, along with comprehensive project metadata that might include specifics about geographic location, technological infrastructure, or operational parameters, you ensure that all parties have access to the same foundational information. This uniformity in the provision of data not only fosters a fair competitive landscape but also enhances the validity of the comparison across different forecasts.
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11. Secure necessary support for thorough assessment
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It is imperative for the team leading the benchmarking initiative to guarantee the allocation of ample resources that are essential for both the provision of relevant data and the execution of a comprehensive validation process. This allocation includes, among other things, access to accurate and up-to-date data, cutting-edge analytical tools, and sufficient manpower to conduct detailed evaluations. By investing in these resources, the benchmark administrator ensures that the data used in the process is of high quality and that the validation methods employed are thorough and capable of accurately assessing the performance of the forecast models being benchmarked. This commitment to resource allocation significantly contributes to the strength and reliability of the benchmarking exercise, as it enables a deep and nuanced analysis of the solar and wind forecasts. It also provides a solid foundation upon which participants can build and refine their forecasting techniques, ultimately leading to improvements in the accuracy and reliability of future forecasts.
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In summary, the success of a forecasting benchmark hinges on clearly defining its goals and using precise metrics for evaluation. A detailed schedule, confidentiality agreements, and a preparatory Q&A phase are essential for aligning participants and ensuring smooth operation. Standardizing data formats and ensuring all participants have equal access to information are critical for fair assessment. Efficient communication and sufficient resource allocation are fundamental for a comprehensive evaluation process.
Now, it's your turn to apply these principles to your benchmarking efforts, setting the stage for innovation and continuous improvement in your operations.
Learn more about our performances for 3 recent benchmarks and test our performance for your portfolio.
To test the quality of our forecasts for one solar plant and/or wind power plant for free, we invite you to use our forecasting trial platform.
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Note:
1. IEA Wind Recommended Practice for the Implementation of Renewable Energy Forecasting Solutions. 270 pp. Elsevier Academic Press, November 1, 2022. ISBN: 9780443186813.
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