Power imbalance prediction in Turkish energy market

Official URL: https://risc01.sabanciuniv.edu/record=b2486402_(Table of contents)

There are potential trading opportunities in predicting energy imbalance in energy markets. The energy imbalance in this study is the hourly energy difference between the final planned production and the real-time consumption at the energy delivery hour. We name it as net loading. From the perspective of an energy trade company (TradeCo), being able to predict the net loading can help to make profitable trades in the intraday market (IM). From the perspective of a generation company (GenCo), being able to predict the net loading can help to optimize price offers it gives to TSO in the balancing power market (BPM). Therefore, being able to predict net loading can provide a competitive edge in the energy market. In this study, net loading is tried to be numerically predicted for (T+1) up to (T+32) hours where T is the prediction hour. Net loading follows an autoregressive pattern and therefore, the developed models are tested against a naïve model that uses the closest available past net loading value as the prediction. The naïve model works performs better than random guess for (T+1) up (T+3). Our champion model beats the naïve model for (T+1) up to (T+32). We have used 15 different machine learning models and tried to improve them in 3 modeling stages. Among the machine learning models, the voting ensemble model at the modeling stage 3 gives the best results. The year 2020 data is used as the main test data and 2018, 2019 data is used for modeling