Summary of Mshw, a Forecasting Library to Predict Short-term Electricity Demand Based on Multiple Seasonal Holt-winters, by Oscar Trull et al.
mshw, a forecasting library to predict short-term electricity demand based on multiple seasonal Holt-Winters
by Oscar Trull, J. Carlos García-Díaz, Angel Peiró-Signes
First submitted to arxiv on: 15 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Econometrics (econ.EM); Applications (stat.AP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a MATLAB toolbox for predicting electricity demand, which implements multiple seasonal Holt-Winters exponential smoothing models and neural network models. The toolbox utilizes discrete interval mobile seasonalities (DIMS) to improve forecasting on special days. The authors demonstrate the effectiveness of their approach by applying it to various electrical systems in Europe. This research contributes to the development of more accurate demand forecasting methods, which are crucial for optimizing electricity market operations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a special tool that helps predict how much energy people will use. Right now, companies have to use complicated software to make predictions, but this new tool is simpler and can get better results. It uses different types of models to make its predictions, like seasonal patterns and artificial intelligence. The authors tested the tool on some European power grids and got good results. This research can help improve the way we predict energy demand in the future. |
Keywords
* Artificial intelligence * Neural network
