Day ahead Photovoltaic Power Forecasting: An Ensemble Method Augmented with Adaptive Weighting and Data Segmentation Technique

Razin Ahmed

Research output: ThesisDoctoral Thesis

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Abstract

The advent of photovoltaics (PV), while promising near limitless green energy, has a serious drawback: the influence of weather conditions on PV output. Accurate and reliable PV power prediction is crucial. Deep-learning algorithms are state-of-the-art methods to counter the non-linear and stochastic nature of PV output. However, such deep-learning models manifest acceptable forecasts only for specific forecast-horizons and data trends. The current study proposes two unique methods: LSTM-ensemble and Meta-Boosting-Network (MBN) using adaptive-weighting and data segmentation-technique. These methods can precisely and consistently predict day-ahead PV output generation. The results establish that the MBN outperforms benchmark models by an average of 50%.
Original languageEnglish
QualificationDoctor of Philosophy
Awarding Institution
  • The University of Western Australia
Supervisors/Advisors
  • Sreeram, Victor, Supervisor
  • Togneri, Roberto, Supervisor
  • Datta, Amitava, Supervisor
Thesis sponsors
Award date6 Jul 2022
DOIs
Publication statusUnpublished - 2022

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