Closure to discussion on "A method for short-term wind power prediction with multiple observation points"

Abstract

Summary form only given. The authors appreciate the valuable comments expressed by the discussers. Our comments are numbered to match the points made in the discussions. 1) It is true that time frames for wind power forecasting are very important for different applications. For example, very short-term wind power forecasting, in terms of seconds to minutes, is important for frequency control, electricity market clearing, and regulation actions. Short-term wind power forecasting, in terms of minutes to hours, is used for economic load dispatch planning and load increment/decrement decisions. Similarly, long-term wind power forecasting, in terms of hours to days, is required for other applications like unit commitment, maintenance scheduling to obtain optimal operating cost, reserve requirement decisions, prediction of power system congestion, and operational security in day-ahead electricity market. In particular, short-term wind power predictions are becoming increasingly important nowadays since the rise of the competitive electricity markets. In this paper, initially the authors have tested the algorithm only for some minutes ahead. However, the prediction for other time frames would be considered in the future. 2) As mentioned in the paper, the optimum values of the model parameters were found by numerical iterations. While making the empirical studies, it was observed that historical time interval during each exercise was different for different data ranges and times. Therefore, the optimal number of historical data points for the given data set was chosen based on manual trial-and-error methods. 3) First of all, we thank the discussers for their appreciation for the proposed idea. The accuracy improvement for wind power prediction was calculated by comparing the results of direction-dependent power curves with a single average power curve for the given data set. It depends on wind velocity as the discusser mentioned. As regards the correlation between the spread of data in Fig. 7 and the spread of data due to wind direction, actually Fig. 7 is shown as a general example to compare a manufacturer's power curve, as in Fig 6, and an actual power curve in a wind farm. In particular, unfortunately it is not the plot of same actual data used in the analyses. Therefore, specifically there is no relation of Fig. 7 with our direction-dependent power curves. As our analyses are based on numerical calculations, we have not worked out analytical relation yet to describe the wind power relation with wind speed and direction together. However, based on these preliminary results and discussers' comments, we will improve this idea of direction-dependent power curves with a generalized relation and conclusion. 4) The wind direction was not predicted using Persistence and Grey model. The proposed method has not been compared to other neural networks or Bayesian-based methods yet. We would consider to improve the proposed technique with automatic and adaptive tuning of the model parameters along with its comparison with other advanced prediction techniques. Once again, we are thankful to the discussers for their interest in our research paper and valuable feedback.