Good or bad wind power measurement data

How do I choose a wind data type?

Given the two different wind data types, we can decide which data to prefer by comparing the wind power curves that are associated with either wind measurement or modeled wind speed. The more scattered these curves map wind speed to wind power, the harder it gets to forecast wind power given wind speed.

How do I find datasets for wind power forecasting?

We compiled the datasets listed in this paper in several different ways: Searching online for datasets, getting in contact with wind power forecasting researchers from every continent to ask for available open-source data and energy data regulations in their region, and searching for papers that work with disclosed data.

Is bias in wind data a problem?

In contrast, the second problem, bias in the data, affects all types of wind data and can be, at least in parts, addressed in the wind power forecasting models themselves. The different wind data measurements include a range of known systematic measurement and modeling errors.

Is 1 year of data enough to predict wind speed?

However, due to yearly seasonality, it is generally assumed that 1 year of data is not sufficient to discover trends in wind speed or wind power. Vargas et al 20 investigate 145 different models and come to the conclusion that most models for long-term forecasting use hourly data (49%).

What are the problems with the different types of wind data?

There are mainly two problems with the different types of wind data, namely, data availability and bias in the data. The first, data availability, mainly affects wind power data (see Figure 1) as pure wind data are open-source in many cases.

What are the advantages of using wind data instead of wind power?

For example, as generated wind power is proportional to wind speed cubed, 30 wind speed mainly determines the performance of an operating wind turbine. However, the main advantage of using wind data instead of wind power data is their high spatial coverage.