Forecast horizon

The forecast horizon is the time period between the time at which the forecast is available and the forecast point in time. Different forecasts are used for different purposes and their forecast horizons depend upon the requirements of the user, stemming from technical and regulatory conditions, and upon the feasibility of forecasting.

From the meteorological and climatological point of view, one can distinguish long-term or seasonal forecasts with a forecast horizon of several months, medium-term forecasts with a range of up to two weeks, short-term forecasts for the next few days, and very short-term forecasts for a forecast horizon of up to one day. Generally, the forecast accuracy decreases with increasing forecast horizon.

For current wind power forecasting, deterministic forecasts are used up to a forecast horizon of three to five days. Essentially, two forecast horizons have to be distinguished: the day-ahead forecast and the short-term forecast. The day-ahead forecast is mainly used for day-ahead power trading. The forecast horizon therefore depends upon the organization of the trading (e.g. the gate closure time and the trading days). An example for a gate closure time of 12.00 am for the next day is shown in Figure 5.3. The NWP model starts running at midnight with the observations from the day before. It finishes calculation around 7.00 am and sends the information to the wind power forecasting system. This usually has a very short calculation time and the results are available a few minutes later. They are analysed and used for trading the power for the next day until at 12.00 am the trading ends. This means that the calculation of the forecast starts 48 hours ahead, counted from the start of the NWP model. If there is no trading during weekends and pubic holidays, lead time for the calculation for the 'day-ahead' trading can actually be 96 hours or longer.

Short-term wind power forecasting is mainly used for intra-day trading and grid operation and security. Its main characteristic is that it utilizes online data from measurements of actual power output and/or wind speed. For very short forecast horizons, this leads to a very important increase in forecast accuracy (see Figure 5.4). Usually, NWP model data and online measurement data are combined for the short-term forecast, giving more weight to the NWP data for longer forecast horizons and more weight to online data for shorter horizons. Very short-term forecasts of up to one or two hours are possible even without NWP model data. For forecast horizons of more than approximately half a day, the online data usually do not add information to the NWP model data and the short-term forecast ends.

NWP model starts

NWP model results sent to forecasting model Wind power forecast results

Day-ahead trading closes il 1

Forecast period


!2h UTC


12h UTC

Figure 5.3 Typical time schedule for wind power forecasting used for day-ahead trading

Figure 5.4 Example time series of online measurement and forecasts of wind power generation in Germany; forecasts with different forecast horizons are shown

Figure 5.4 Example time series of online measurement and forecasts of wind power generation in Germany; forecasts with different forecast horizons are shown

Regional upscaling

A wind power forecast for a larger region with many wind farms is usually made by forecasting only some of the wind farms and by extrapolating their power output to the whole region - often called regional upscaling. This minimizes the effort involved in making the forecasts and reduces the amount of data needed from NWP models as input. The accuracy of the forecasts does not decrease much since wind farms close to each other show a similar behaviour. However, it is important that the wind farms selected for forecasting are representative of all wind farms to which their output is extrapolated.

Different algorithms can be used for upscaling. Their main function is to calculate the output of all wind farms of the area from the known (or forecast) output of the representative wind farms. In the Wind Power Management System (WPMS), developed by ISET in Germany, the following mechanism is used. The area of interest is subdivided into grid squares. For each of these grid squares, the installed capacity of wind farms, their coordinates and hub heights, and the roughness of the terrain are known. This information is compiled from a database of all wind turbines in Germany, which includes:

• installed power;

• surface roughness;

• date of erection and dismantling.

Figure 5.5 Grid squares used by the Wind Power Management System for regional upscaling

Figure 5.5 Grid squares used by the Wind Power Management System for regional upscaling

Figure 5.5 shows the grid squares for Germany as an example. The size of the squares shows information about installed capacity: the smallest squares are 1 megawatt (MW) to 13MW, and the largest squares are 131MW to 146MW. The WPMS calculates the power output of the wind turbines in each grid square by using the forecast power output of the representative wind farms.

The closer a reference wind farm is to the grid square, the greater is its influence (see Figure 5.6). Considering a case with i grid squares and j representative wind farms, the power output of the whole region Ptotal is the sum of the power output Pi from all its grid squares:

The power output of one grid square is calculated from the weighted power outputs of all representative wind farms:

Here, Pj is the power output of representative wind farm j, normalized with its installed power, and ki is a normalization factor. The weighting factors A^are calculated as:

where PIPi is the installed power in grid square i, Sjis the distance between a representative wind farm and the grid square, and S0 is a spatial correlation parameter that has to be determined empirically.

The normalization factor ki makes sure that the sum of all weighting factors equals 1:

Renewable Energy Eco Friendly

Renewable Energy Eco Friendly

Renewable energy is energy that is generated from sunlight, rain, tides, geothermal heat and wind. These sources are naturally and constantly replenished, which is why they are deemed as renewable.

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