Mpofu, K.; Adenuga, O.T.; Popoola, O.M.; Mathebula, A.A. LightGBM and SVM algorithms for Predicting Synthetic Load Profiles Using a Non-Intrusive Approach. Preprints2023, 2023080257. https://doi.org/10.20944/preprints202308.0257.v1
APA Style
Mpofu, K., Adenuga, O.T., Popoola, O.M., & Mathebula, A.A. (2023). LightGBM and SVM algorithms for Predicting Synthetic Load Profiles Using a Non-Intrusive Approach. Preprints. https://doi.org/10.20944/preprints202308.0257.v1
Chicago/Turabian Style
Mpofu, K., Olawale M Popoola and And Abel Mathebula. 2023 "LightGBM and SVM algorithms for Predicting Synthetic Load Profiles Using a Non-Intrusive Approach" Preprints. https://doi.org/10.20944/preprints202308.0257.v1
Abstract
Due to related uncertainties brought by changes in energy consumption and the integration of rooftop photovoltaic systems, the accuracy and availability of load profiling data are difficult to achieve. However, project managers and planners use a precise load profile as a crucial tool when determining whether the feeder must be updated or de-loaded. The paper aims to create a non-intrusive monitoring system that predicts the synthetic load profiles behavior of energy consumption using a Light Gradient Boosted and Support Vector Machine (SVM) machine learning technique. The most effective ML algorithm is chosen, and it has the potential to be assessed and verified using validation curves, residuals model generation, and prediction error metrics based on the following key statistical indicators: Mean Absolute Error, Mean Square Error, Root Mean Square Error, R-Square, Root Mean Squared Logarithmic Error, and Mean Absolute Percentage Error. The most effective ML has the potential to be assessed and verified using validation curves, residual model generation, and prediction error metrics based on the following key statistical indicators. The result shows that the estimation of the Irms avg -based on LightGBM is more accurate than the SVM because of its quick, economical, and difficult to overfit, particularly with high-dimensional data, speed, and efficiency of the MAE (3.0698), MSE (15.2757), RMSE (3.9020), RMSLE (0.1433), and MAPE (0.0049) respectively. Additionally, the machine learning model shows that, when compared to the SVM, the LightGBM model had the highest accurate prediction, with R-Square values of 89.8%, 90.3%, and 88.5% for Irms_A_avg, Irms_B_avg, and Irms_C_avg, respectively for Brakfontein Substation supply region, and best represents a diverse customer base.
Keywords
Light Gradient Boosted; Machine learning; Non-Intrusive Approach; electric load forecasting; Support Vector Machine; Synthetic Load Profile.
Subject
Engineering, Industrial and Manufacturing Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
