首页|Reports from University of South Africa Advance Knowledge in Machine Learning (F orecasting of Residential Energy Utilisation Based on Regression Machine Learnin g Schemes)
Reports from University of South Africa Advance Knowledge in Machine Learning (F orecasting of Residential Energy Utilisation Based on Regression Machine Learnin g Schemes)
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New research on artificial intelligenc e is the subject of a new report. According to news reporting from Florida, Sout h Africa, by NewsRx journalists, research stated, "Energy utilisation in residen tial dwellings is stochastic and can worsen the issue of operational planning fo r energy provisioning." Financial supporters for this research include National Research Foundation of S outh Africa. Our news correspondents obtained a quote from the research from University of So uth Africa: "Additionally, planning with intermittent energy sources exacerbates the challenges posed by the uncertainties in energy utilisation. In this work, machine learning regression schemes (random forest and decision tree) are used t o train a forecasting model. The model is based on a yearly dataset and its subs et seasonal partitions. The dataset is first preprocessed to remove inconsistenc ies and outliers. The performance measures of mean absolute error (MAE), mean sq uare error (MSE) and root mean square error (RMSE) are used to evaluate the accu racy of the model. The results show that the performance of the model can be enh anced with hyperparameter tuning."
University of South AfricaFloridaSou th AfricaAfricaCyborgsEmerging TechnologiesMachine Learning
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