Sleiman, A.; Su, W. Combined K-Means Clustering with Neural Networks Methods for PV Short-Term Generation Load Forecasting in Electric Utilities. Energies 2024, 17, 1433, doi:10.3390/en17061433.
Sleiman, A.; Su, W. Combined K-Means Clustering with Neural Networks Methods for PV Short-Term Generation Load Forecasting in Electric Utilities. Energies 2024, 17, 1433, doi:10.3390/en17061433.
Sleiman, A.; Su, W. Combined K-Means Clustering with Neural Networks Methods for PV Short-Term Generation Load Forecasting in Electric Utilities. Energies 2024, 17, 1433, doi:10.3390/en17061433.
Sleiman, A.; Su, W. Combined K-Means Clustering with Neural Networks Methods for PV Short-Term Generation Load Forecasting in Electric Utilities. Energies 2024, 17, 1433, doi:10.3390/en17061433.
Abstract
The power system has rapidly grown and expanded over the past decades and has been experiencing major changes and challenges. The increase in energy demand and the modern advancements in the smart grid, such as solar and wind energies and electric vehicles, have led to complexity and complications for utilities. A further layer of complexity and difficulty was added by the rapid expansion of behind-the-meter (BTM) photovoltaic (PV) systems with various designs and characteristic features. The rapid increase and the invisible solar power (BTM) have led to fluctuations in power grid stability and reliability and to inefficiency. Accurate forecasting of load generation will help to assure optimal planning, minimize the negative effects of the PV systems, and minimize the operational and maintenance costs. The authors propose a solution that uses combinations of K-means clustering with neural network machine learning models, AMI real-world PV load generation, and weather data to forecast the generation load at customer locations to achieve a 2.49% error between actual and predicted generation load.
Keywords
machine learning; neural networks; PV power forecasting; smart meters; solar energy
Subject
Engineering, Electrical and Electronic 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.
