Solar and Wind Forecasting
Wind and solar resource forecasting predicts future energy output through numerical weather prediction models and statistical approaches. Resource forecasting is relatively new compared with system load forecasting, and it is not yet as accurate.
Experience suggests that the overall shape of wind energy production can be predicted most of the time, but significant errors in the level and timing of wind energy production can occur. Therefore, electric power system operators are interested in both the uncertainty of a particular forecast and the overall accuracy of forecasts in general.
The wind forecasts for the near term tend to be more accurate than forecasts for longer terms. For example, for a single wind power plant, forecasts one to two hours ahead can achieve accuracy levels of approximately 5%–7% mean absolute error relative to installed wind capacity, but this increases to 20% for day-ahead forecasts.
Wind forecasting also enjoys strong geographical aggregation benefits. Aggregating wind power plants over a region significantly reduces forecasting error. In Germany, for example, forecast error was reduced 50% from a single site prediction to a regional prediction of measured power production. In other research conducted in Germany, typical wind-forecast errors for representative wind power forecasts for a single wind project are 10%–15% root mean square error of installed wind capacity but drop to 6%–8% for day-ahead wind forecasts for a single control area and to 5%–7% for day-ahead wind forecasts for all of Germany. Combining different wind-forecasting models into an ensemble wind forecast can also improve wind-forecasting accuracy by up to 20%, as measured by root mean square error. More importantly, with aggregation, the impact of forecast errors on individual wind plants is not as severe because the aggregate forecast of all wind plants drives the committing and scheduling of generation.
The forecasting of solar energy production faces issues similar to those of wind. However, solar forecasting has significant predictability because the sun’s path through the sky is known. Nonetheless, solar resource forecasting is not as mature.
NREL is addressing solar and wind forecasting issues on the transmission system. For more information, see the following publications:
Do Wind Forecasts Make Good Generation Schedules?
Energy market scheduling conventions can needlessly increase a wind balancing area's regulation requirements. This economic inefficiency can be eliminated once it is recognized. The paper provides a detailed discussion of these issues.
Wind Power Forecasting Error Distributions over Multiple Timescales
This paper examines the shape of the persistence model error distribution for 10 wind plants over multiple timescales. Comparisons are made between the experimental distribution shape and that of the normal distribution.
Value of Wind Power Forecasting
This study evaluates the operating cost impacts of improved day-ahead wind forecasts.
Ramp Forecasting Performance from Improved Short-Term Wind Power Forecasting
This paper evaluates the performance of improved short-term wind power ramp forecasting. The study was performed for the Electric Reliability Council of Texas by comparing the experimental Wind Forecasting Improvement Project forecast to the current short-term wind power forecast.
Investigating the Correlation Between Wind and Solar Power Forecast Errors in the Western Interconnection
This paper investigates the correlation between wind and solar power forecast errors.
Metrics for Evaluating the Accuracy of Solar Power Forecasting
This paper presents a suite of generally applicable and value-based metrics for solar forecasting for a comprehensive set of scenarios. In addition, a comprehensive framework is developed to analyze the sensitivity of the proposed metrics.
Analysis of Variability and Uncertainty in Wind Power Forecasting: An International Comparison
This paper investigates the variability and uncertainty in wind forecasting for multiple power systems in six countries.
Examining Information Entropy Approaches as Wind Power Forecasting Performance Metrics
This paper examines the parameters associated with the calculation of the Rényi entropy in order to further the understanding of its application to assessing wind power forecasting errors.
Wind Power Forecasting Error Distributions: An International Comparison
This paper provides an international comparison of the distribution of wind power forecasting errors from operational systems, based on real forecast data.
A Comparison of Wind Power and Load Forecasting Error Distributions
This paper examines the distribution of errors from operational forecasting systems in two different Independent System Operator regions for both wind power and load forecasts at the day-ahead timeframe.
Characterizing and Modeling Wind Power Forecast Errors from Operational Systems for Use in Wind Integration Planning Studies
This paper statistically characterizes and models the wind power forecast errors from three different operational forecasting systems at multiple timescales.