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Interpolating high granularity solar generation and load consumption data using super resolution generative adversarial network

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  • Tang, Rui
  • Dore, Jonathon
  • Ma, Jin
  • Leong, Philip H.W.

Abstract

The vast majority of commonly accessible photovoltaics (PV) generation and load consumption datasets have low temporal resolutions, leading to inaccuracies in the modeling and optimisation of PV-integrated battery systems. This study addresses this problem by proposing an interpolation model based on a super resolution generative adversarial network (SRGAN) that generates 5-minute PV and load power data from 30-minute/hourly temporal resolutions. The proposed approach is validated by two different datasets including large amounts of residential data and compared to an alternative predictive model. The results indicate that the model can adequately capture the targeted data distributions and temporal characteristics with negligible statistical differences from the measured high resolution data. Moreover, it performs consistently across different types of PV/load profiles and on average it results in 0.32% and 0.28% normalised root mean squared errors (NRMSEs) in daily totals of 5-minute PV and load power values when using hourly data as inputs. Under a time-of-use (ToU) tariff, the interpolated 5-minute data leads to 44.7% and 41.7% error reductions compared to using hourly data for estimating electricity costs and battery saving potentials of a PV battery system. Hence, the proposed model can be potentially applied in a battery sizing tool to obtain more accurate sizing results when only low resolution data is available.

Suggested Citation

  • Tang, Rui & Dore, Jonathon & Ma, Jin & Leong, Philip H.W., 2021. "Interpolating high granularity solar generation and load consumption data using super resolution generative adversarial network," Applied Energy, Elsevier, vol. 299(C).
    • Handle: RePEc:eee:appene:v:299:y:2021:i:c:s0306261921007108
    DOI: 10.1016/j.apenergy.2021.117297
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    References listed on IDEAS

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    1. Wang, Kejun & Qi, Xiaoxia & Liu, Hongda, 2019. "A comparison of day-ahead photovoltaic power forecasting models based on deep learning neural network," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    2. Hawkes, Adam & Leach, Matthew, 2005. "Impacts of temporal precision in optimisation modelling of micro-Combined Heat and Power," Energy, Elsevier, vol. 30(10), pages 1759-1779.
    3. Tang, Rui & Yildiz, Baran & Leong, Philip H.W. & Vassallo, Anthony & Dore, Jonathon, 2019. "Residential battery sizing model using net meter energy data clustering," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    4. Beck, T. & Kondziella, H. & Huard, G. & Bruckner, T., 2016. "Assessing the influence of the temporal resolution of electrical load and PV generation profiles on self-consumption and sizing of PV-battery systems," Applied Energy, Elsevier, vol. 173(C), pages 331-342.
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    Cited by:

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    3. Wei Wei & Li Ye & Yi Fang & Yingchun Wang & Xi Chen & Zhenhua Li, 2023. "Optimal Allocation of Energy Storage Capacity in Microgrids Considering the Uncertainty of Renewable Energy Generation," Sustainability, MDPI, vol. 15(12), pages 1-17, June.
    4. Carlos Antônio Rufino Júnior & Eleonora Riva Sanseverino & Pierluigi Gallo & Murilo Machado Amaral & Daniel Koch & Yash Kotak & Sergej Diel & Gero Walter & Hans-Georg Schweiger & Hudson Zanin, 2024. "Unraveling the Degradation Mechanisms of Lithium-Ion Batteries," Energies, MDPI, vol. 17(14), pages 1-51, July.
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    6. Li, Yang & Han, Meng & Shahidehpour, Mohammad & Li, Jiazheng & Long, Chao, 2023. "Data-driven distributionally robust scheduling of community integrated energy systems with uncertain renewable generations considering integrated demand response," Applied Energy, Elsevier, vol. 335(C).

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