This study assesses different machine learning and statistical methods to perform short-term point electricity price forecasting on the maximum buying and minimum selling Intraday (ID) market prices for each hour. The study begins with a primer and background on the current state of the electricity markets and why the need to trade on an ID market is growing. The study examines different time-series forecasting methods using available exogenous electricity market data, such as the Day-ahead (DA) market price data and the ID prices. The models are evaluated on a set of error metrics, and for comparison, is a baseline constructed by using the DA price for the same hour as the forecast for the targets. The models evaluated are a Deep Neural Nets (DNN) model, an Autoregressive (AR) model and a XGBoost model. Further, a data scaling and transformation method, referred to as the Median-normalised asinh Transform (asinh1), improves the performance of all the models except the baseline, compared to Standardisation scaling (StdSc). The regularised AR model performed best, with the lowest overall scores on the metrics. However, the DNN model can best capture outlier patterns in the minimum selling ID prices. Throughout the study, it turns out that the buying price patterns and outliers are harder to forecast than the selling prices. This study aims to provide insights into the performance of different models and generally contribute to decreasing the knowledge gap between ID price forecasting and other electricity entities forecasting.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:uu-530683 |
| Date | January 2024 |
| Creators | Englund, Axel |
| Publisher | Uppsala universitet, Avdelningen för beräkningsvetenskap |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | UPTEC F, 1401-5757 ; 24017 |
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