Summary of Water and Electricity Consumption Forecasting at An Educational Institution Using Machine Learning Models with Metaheuristic Optimization, by Eduardo Luiz Alba et al.
Water and Electricity Consumption Forecasting at an Educational Institution using Machine Learning models with Metaheuristic Optimization
by Eduardo Luiz Alba, Matheus Henrique Dal Molin Ribeiro, Gilson Adamczuk, Flavio Trojan, Erick Oliveira Rodrigues
First submitted to arxiv on: 25 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 A comparison between two machine learning models, Random Forest (RF) and Support Vector Regression (SVR), is proposed for water and electricity consumption forecasting at the Federal Institute of Paraná-Campus Palmas. The study optimizes hyperparameters using the Genetic Algorithm (GA) and evaluates performance measures such as absolute percentage errors and root mean squared error. Results show that the Random Forest model achieves superior performance in forecasting water and electricity consumption over a 12-step horizon, while the integration of climatic variables often leads to diminished forecasting accuracy. This study highlights the importance of considering exogenous features, such as climatic variables, when predicting water and electricity consumption. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper compares two machine learning models, Random Forest and Support Vector Regression, to predict water and electricity usage at a university campus in Brazil. The researchers tested these models using data from the past five years and found that one model worked better than the other. They also looked at how using weather information affected the predictions. Overall, this study shows that predicting water and electricity usage can be challenging and may require more research. |
Keywords
» Artificial intelligence » Machine learning » Random forest » Regression
