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Complementary thermal energy generation associated with renewable energies using Artificial Intelligence

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  • Hammerschmitt, Bruno Knevitz
  • Guarda, Fernando Guilherme Kaehler
  • Lucchese, Felipe Cirolini
  • Abaide, Alzenira da Rosa

Abstract

This work proposes a short-term modeling and simulation structure to predict the electrical energy generation capacity of an electrical system with centralized generation and load presenting a diversified mix of energy sources. This will be accomplished by analyzing the generation forecasting and highlighting the energy complementarity imposed on available and simulated thermal generation, taking into account operation historical series. In order to model the electrical energy generation forecasting, a structure of Multi-layer Perceptron (MLP) artificial neural networks was used and multi scenarios (critical, ideal and optimistic) were generated by the Monte Carlo (MC) method. The forecasting results obtained for MLP had for mean absolute error and root mean square error respectively the rates of 3.22% and 4.01% for hydro generation, and 5.36% and 6.31% for wind generation. Thus, it was possible to estimate the available complementary thermal generation and the natural gas thermal generation that were simulated to meet the system load. With the results from joining MLP and MC, it was possible to quantify the availability of energy in front generation system plants to adverse conditions and propose complementation, emphasizing the importance of the forecasting model to aid on the planning and operation of electrical systems.

Suggested Citation

  • Hammerschmitt, Bruno Knevitz & Guarda, Fernando Guilherme Kaehler & Lucchese, Felipe Cirolini & Abaide, Alzenira da Rosa, 2022. "Complementary thermal energy generation associated with renewable energies using Artificial Intelligence," Energy, Elsevier, vol. 254(PB).
    • Handle: RePEc:eee:energy:v:254:y:2022:i:pb:s0360544222011677
    DOI: 10.1016/j.energy.2022.124264
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    2. Li, Jingmiao & Liu, Dehong, 2023. "Carbon price forecasting based on secondary decomposition and feature screening," Energy, Elsevier, vol. 278(PA).

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