Difference between revisions of "Projects:2020s2-7522 Finding a relationship between predictability measure and actual prediction error using self-similarity measure"
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| − | Predictability measure is an important aspect of judging the accuracy of prediction, and the value reflects the predicting precision. A small prediction error indicating a higher prediction accuracy. | + | Predictability measure is an important aspect of judging the accuracy of prediction, and the value reflects the predicting precision. A small prediction error indicating a higher prediction accuracy, which is significant and useful in many fields. |
| − | == Introduction == | + | Based on the existing PV power data, classifying method and analysing method, a relationship between predictability measure (not only restrict to Hurst Exponent) and prediction error will be discussed in this project. Based on the sorted data base, use MATLAB to perfome the mathematical operations and analysing them through time series regression model, exponential smoothing model and ARIMA model. Finally, analyse and compare the result given by the mathematical operations and forecasting models, then discussing the accuracy of power forecasting and pointing out the error analysis. |
| + | == Introduction == | ||
=== Project team === | === Project team === | ||
Revision as of 17:11, 20 September 2020
Predictability measure is an important aspect of judging the accuracy of prediction, and the value reflects the predicting precision. A small prediction error indicating a higher prediction accuracy, which is significant and useful in many fields. Based on the existing PV power data, classifying method and analysing method, a relationship between predictability measure (not only restrict to Hurst Exponent) and prediction error will be discussed in this project. Based on the sorted data base, use MATLAB to perfome the mathematical operations and analysing them through time series regression model, exponential smoothing model and ARIMA model. Finally, analyse and compare the result given by the mathematical operations and forecasting models, then discussing the accuracy of power forecasting and pointing out the error analysis.
Contents
Introduction
Project team
Project students
- Erli Yin
- Xuan Hu
Supervisors
- Dr Ali Pourmousavi Kani
- Professor Mathias Baumert
