This degree project undertakes a detailed examination of various algorithms used in Very Short-Term Load Forecasting (VSTLF) within network control systems, prioritizing forecasting accuracy and computational efficiency as critical evaluation criteria. The research comprehensively assesses a range of forecasting methods, including statistical models, machine learning algorithms, and advanced deep learning techniques, aiming to highlight their respective advantages, limitations, and suitability for different operational contexts. The study conducts a detailed analysis by comparing essential performance metrics such as Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and execution time, before and after implementing adjustments to the formulations. This approach highlights how optimization strategies enhance the effectiveness of the models. Notably, the study identifies Support Vector Machine (SVM) and Multiple Linear Regression as frontrunners in terms of balancing accuracy with computational demand, making them particularly suitable for real-time forecasting needs. Meanwhile, Long Short-Term Memory (LSTM) networks demonstrate a commendable ability to capture complex, non-linear data patterns, albeit at a higher computational cost. The degree project further explores the sensitivity of these forecasting models to parameter adjustments, revealing a nuanced landscape where strategic modifications can significantly enhance model performance. This degree project not only contributes to the ongoing discourse on optimizing VSTLF algorithms but also provides actionable insights for stakeholders in the energy sector, aiming to facilitate the development of more reliable, efficient, and sustainable power system operations.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:mdh-67956 |
| Date | January 2024 |
| Creators | Al Madani, Mhd Rami |
| Publisher | Mälardalens universitet, Akademin för ekonomi, samhälle och teknik |
| 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 |
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