Projects:2020s2-7522 Finding a relationship between predictability measure and actual prediction error using self-similarity measure

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.

Introduction

The number of large-scale Photovoltaic (PV) and wind farms is rapidly growing in Australia and all around the world. The majority of which came from small-scale rooftop PV. More than two million, or 21 percent, of Australian households now have rooftop solar PV. However, there are many factors affecting the stability of renewable energy generation. Many researchers have developed forecast techniques to try to accurately predict the generation. However, it is still not accurate enough. The purpose of this project is to use predictability measures (such as Hurst exponent) and forecast models to predict the given data to evaluate the predictability of PV generation. Thus, find the relationship between predictability and actual forecast error.

Project team

Project students

• Erli Yin
• Xuan Hu

Supervisors

• Dr Ali Pourmousavi Kani
• Professor Mathias Baumert