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Forecasting low voltage distribution network demand profiles using a pattern recognition based expert system

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  • Bennett, Christopher J.
  • Stewart, Rodney A.
  • Lu, Jun Wei

Abstract

The advent of distributed renewable energy supply sources and storage systems has placed a greater degree of focus on the operations of the LV (low voltage) electricity distribution network. However, LV networks are characterised by having much higher variability in time series demand meaning that modelling techniques solely relying on iterative forecasts to produce a next day demand profile forecast are insufficient. To cater for the complexity of LV network demand, a novel hybrid expert system comprised of three modules, namely, correlation clustering, discrete classification neural network, and a post-processing procedure was developed. The system operates by classifying a set of key variables associated with a future day and refining a recalled historical demand profile as the forecast. The expert system exhibited high hindcast accuracy when trained with a residential LV transformer's demand data with R2 values ranging from 0.86 to 0.87 and MAPE (mean absolute percentage error) ranging from 11% to 12% across the three phases of the network. Under simulated real world conditions the R2 statistic reduced slightly to 0.81–0.84 and the MAPE increased to 12.5–13.5%. Future work will involve integrating the developed expert system for forecasting next day demand in an LV network into a comprehensive distributed energy resource management algorithm.

Suggested Citation

  • Bennett, Christopher J. & Stewart, Rodney A. & Lu, Jun Wei, 2014. "Forecasting low voltage distribution network demand profiles using a pattern recognition based expert system," Energy, Elsevier, vol. 67(C), pages 200-212.
    • Handle: RePEc:eee:energy:v:67:y:2014:i:c:p:200-212
    DOI: 10.1016/j.energy.2014.01.032
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    References listed on IDEAS

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    Cited by:

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    7. Haben, Stephen & Giasemidis, Georgios & Ziel, Florian & Arora, Siddharth, 2019. "Short term load forecasting and the effect of temperature at the low voltage level," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1469-1484.
    8. Liu, Xiufeng & Nielsen, Per Sieverts, 2016. "A hybrid ICT-solution for smart meter data analytics," Energy, Elsevier, vol. 115(P3), pages 1710-1722.
    9. Cheng-Wen Lee & Bing-Yi Lin, 2016. "Application of Hybrid Quantum Tabu Search with Support Vector Regression (SVR) for Load Forecasting," Energies, MDPI, vol. 9(11), pages 1-16, October.
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    11. Quan, Hao & Srinivasan, Dipti & Khosravi, Abbas, 2014. "Uncertainty handling using neural network-based prediction intervals for electrical load forecasting," Energy, Elsevier, vol. 73(C), pages 916-925.
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    13. Haben, Stephen & Arora, Siddharth & Giasemidis, Georgios & Voss, Marcus & Vukadinović Greetham, Danica, 2021. "Review of low voltage load forecasting: Methods, applications, and recommendations," Applied Energy, Elsevier, vol. 304(C).
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