Why is my project failing to meet budget?

Data can help you understand the factors impacting your project's budget

It's not uncommon for production budgets to deviate. According to Clir’s industry benchmarks, the average budget shortfall was 6.3%, depending on project design, type, location, and technology. This leads to a lack of confidence in future investments and adds up to millions of dollars in lost capital.

By harnessing industry data, Clir offers deeper insight into loss factors and underperformance, allowing owners to measure project performance against industry peers. This insight can be instrumental in understanding why projects are missing budget and can be leveraged to improve the accuracy of budget forecasting for existing or future assets.

Industry-wide budget forecasting issues

While underperformance against budget is a prevalent issue, overlooking these inaccuracies can cause significant issues. Inaccurate budgets directly impact financial models and lead to lower than expected returns.

Based on our industry intelligence from assets and owners across the globe, there are common issues that contribute to inaccurate production budget models:

• Inaccurate pre-construction estimates: Without operational project data, pre-construction budgets are based on energy yield assumptions that rely on overly optimistic assumptions and biased uncertainty. Estimates are generated from meteorological towers on-site and out-dated technology assumptions. This may not be reflective of the true terrain, the wind resource at hub height or the technology performance.
• Assumptions on long-term predictions: It can be challenging to predict long-term performance without years of data. Assumptions are used to estimate how technologies will perform in the region, and the challenges, losses, and risks it will face across the project lifecycle. It also fails to account for possible optimizations throughout the project lifespan that can improve performance. This results in inaccurate long-term predictions.

How to leverage data to improve budgets

1. Use industry benchmarks for pre-construction: Employing industry benchmarking data helps to reduce the uncertainty with the P50, improving the accuracy of budget forecasts. Clir’s software leverages industry data in energy yield assessments for accurate loss factor assumptions. This is done by using industry data from peer farms — based on OEM and region — instead of generic assumptions.
2. Leverage peer and industry data for performance predictions: There will always be uncertainty when predicting long term production, but industry data can help. Clir’s software creates benchmarks based on region, OEM models and O&M strategies to help owners understand farm performance in an industry context. This can help to understand how assets will age to improve certainty for long-term performance predictions.
3. Understand optimization opportunities: Once losses and underperformance are understood, owners can leverage data to understand where optimization efforts should be focused. With insight into farm production, risk mitigation strategies and industry performance, Clir’s platform empowers owners with an optimization roadmap to improve production, helping projects get closer to meeting budget.

Clir's industry data

Budget forecasting in the development stage can be challenging due to a lack of concrete data. Leveraging Clir's industry and peer data, and ensuring that budgets are being reforcasted with operational project data, can help stakeholders establish more accurate targets.?

Employing robust energy yield assessments, and using farm and industry data for benchmarking performance and identifying losses can offer valuable insights. This information not only sheds light on why projects might be falling short of budgets, but also provides actionable strategies to enhance performance.

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