Fast Fourier Transforms for Microgrid Climate Computing

Paolo Fracas*, Edwin Zondervan

*Corresponding author for this work

Research output: Contribution to journalConference articleAcademicpeer-review

1 Citation (Scopus)

Abstract

Solar panels and wind turbines are key technologies for a sustainable low-carbon energy transition. The large diffusion of weather-dependent renewable energy generators face the challenge to fit the demand of uncertain loads with the most appropriate mix of distributed energy resources. The availability of accurate climate variables projections is essential to identify the best combination among distributed generators, energy storages and power loads in each geographical location. From the European Centre for Medium-Range Weather Forecasts (ECMWF) datasets of several climate variables are available for renewable energy resources yield forecasting. This paper presents different approaches to manipulate ECMWF datasets by combining Fast Fourier Transform with polynomial and forest tree regression models to predict climate variables over a one-year period, typical for microgrid simulations. An example of climate datasets forecasts related to the city of Bremen is presented with the evaluation of performances during test and training scenarios phases.

Original languageEnglish
Pages (from-to)1657-1662
Number of pages6
JournalComputer aided chemical engineering
Volume46
DOIs
Publication statusPublished - 2019
Externally publishedYes
Event29th European Symposium on Computer Aided Process Engineering, ESCAPE 2019 - Eindhoven, Netherlands
Duration: 16 Jun 201919 Jun 2019
Conference number: 29

Keywords

  • Climate Monthly Datasets
  • ECMW
  • FFT
  • IFFT
  • Renewable Energy Systems

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