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Combined K-Means Clustering with Neural Networks Methods for PV Short-Term Generation Load Forecasting in Electric Utilities

Author

Listed:
  • Alex Sleiman

    (DTE Electric, Detroit, MI 48226, USA
    Department of Electrical and Computer Engineering, University of Michigan-Dearborn, Dearborn, MI 48128, USA)

  • Wencong Su

    (Department of Electrical and Computer Engineering, University of Michigan-Dearborn, Dearborn, MI 48128, USA)

Abstract

The power system has undergone significant growth and faced considerable challenges in recent decades, marked by the surge in energy demand and advancements in smart grid technologies, including solar and wind energies, as well as the widespread adoption of electric vehicles. These developments have introduced a level of complexity for utilities, compounded by the rapid expansion of behind-the-meter (BTM) photovoltaic (PV) systems, each with its own unique design and characteristics, thereby impacting power grid stability and reliability. In response to these intricate challenges, this research focused on the development of a robust forecasting model for load generation. This precision forecasting is crucial for optimal planning, mitigating the adverse effects of PV systems, and reducing operational and maintenance costs. By addressing these key aspects, the goal is to enhance the overall resilience and efficiency of the power grid amidst the evolving landscape of energy and technological advancements. The authors propose a solution leveraging LSTM (long short-term memory) model for a forecasting horizon up to 168 hours. This approach incorporates combinations of K-means clustering, automated meter infrastructure (AMI) real-world PV load generation, weather data, and calculated solar positions to forecast the generation load at customer locations to achieve a 5.7% mean absolute error between the actual and the predicted generation load.

Suggested Citation

  • Alex Sleiman & Wencong Su, 2024. "Combined K-Means Clustering with Neural Networks Methods for PV Short-Term Generation Load Forecasting in Electric Utilities," Energies, MDPI, vol. 17(6), pages 1-27, March.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:6:p:1433-:d:1358146
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    References listed on IDEAS

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    1. Gao, Bixuan & Huang, Xiaoqiao & Shi, Junsheng & Tai, Yonghang & Zhang, Jun, 2020. "Hourly forecasting of solar irradiance based on CEEMDAN and multi-strategy CNN-LSTM neural networks," Renewable Energy, Elsevier, vol. 162(C), pages 1665-1683.
    2. Suryanarayana, Gowri & Lago, Jesus & Geysen, Davy & Aleksiejuk, Piotr & Johansson, Christian, 2018. "Thermal load forecasting in district heating networks using deep learning and advanced feature selection methods," Energy, Elsevier, vol. 157(C), pages 141-149.
    3. Robert Tibshirani & Guenther Walther & Trevor Hastie, 2001. "Estimating the number of clusters in a data set via the gap statistic," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(2), pages 411-423.
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    1. Quota Alief Sias & Rahma Gantassi & Yonghoon Choi & Jeong Hwan Bae, 2024. "Recurrence Multilinear Regression Technique for Improving Accuracy of Energy Prediction in Power Systems," Energies, MDPI, vol. 17(20), pages 1-15, October.

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