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Fuzzy data compression for energy optimization models

Author

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  • Groscurth, H.-M.
  • Kress, K.-P.

Abstract

Calculating energy savings and emission–reduction potentials for municipal energy systems requires computer models with high spatial and temporal disaggregation. Consequently, the computational effort necessary to run such models is considerable. For the optimization models ecco, ecco-solar and deeco, this problem has so far been solved by assuming that the time intervals considered are independent of each other and may, therefore, be optimized consecutively. For minimization of primary energy inputs and emission of pollutants, this approach is sufficient. Optimization of monetary costs and placing of intertemporal emission limits, however, is not possible. Based on fuzzy-set theory, we develop a method with which the up to 8760 input-data sets of the model can be compressed by 1–2 orders of magnitude such that the remaining ones may then be optimized simultaneously. Test runs with original and compressed data sets show good agreement, thus proving the functioning of the method.

Suggested Citation

  • Groscurth, H.-M. & Kress, K.-P., 1998. "Fuzzy data compression for energy optimization models," Energy, Elsevier, vol. 23(1), pages 1-9.
    • Handle: RePEc:eee:energy:v:23:y:1998:i:1:p:1-9
    DOI: 10.1016/S0360-5442(97)00060-1
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    Cited by:

    1. Yong Zeng & Yanpeng Cai & Guohe Huang & Jing Dai, 2011. "A Review on Optimization Modeling of Energy Systems Planning and GHG Emission Mitigation under Uncertainty," Energies, MDPI, vol. 4(10), pages 1-33, October.
    2. Jonghoon Ahn, 2020. "Performance Analyses of Temperature Controls by a Network-Based Learning Controller for an Indoor Space in a Cold Area," Sustainability, MDPI, vol. 12(20), pages 1-17, October.
    3. Behrang, M.A. & Assareh, E. & Ghalambaz, M. & Assari, M.R. & Noghrehabadi, A.R., 2011. "Forecasting future oil demand in Iran using GSA (Gravitational Search Algorithm)," Energy, Elsevier, vol. 36(9), pages 5649-5654.
    4. Suganthi, L. & Iniyan, S. & Samuel, Anand A., 2015. "Applications of fuzzy logic in renewable energy systems – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 585-607.
    5. Ahn, Jonghoon & Chung, Dae Hun & Cho, Soolyeon, 2018. "Energy cost analysis of an intelligent building network adopting heat trading concept in a district heating model," Energy, Elsevier, vol. 151(C), pages 11-25.

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