IrMaSet: Intelligent Weather Forecaster System for Hyper-Local Renewable Energies (2024)

Zaidan, M. A., Motlagh, N. H., Zakeri, B., Petaja, T., Kulmala, M., & Tarkoma, S. (Angenommen/Im Druck). IrMaSet: Intelligent Weather Forecaster System for Hyper-Local Renewable Energies. IEEE Consumer Electronics Magazine, 1-13. https://doi.org/10.1109/MCE.2024.3382438

Zaidan, Martha Arbayani ; Motlagh, Naser Hossein ; Zakeri, Behnam et al. / IrMaSet : Intelligent Weather Forecaster System for Hyper-Local Renewable Energies. in: IEEE Consumer Electronics Magazine. 2024 ; S. 1-13.

@misc{17ea1c196cd645818d9d44641a5f8973,

title = "IrMaSet: Intelligent Weather Forecaster System for Hyper-Local Renewable Energies",

abstract = "Weather forecasting plays a vital role in estimating energy generation from variable renewable energy sources (VRES). Current weather forecast methods for estimating energy from VRES are typically at a scale of a few kilometers, which is not the fine spatial resolution needed for distributed energy planning. In this paper, we propose IrMaSet (an intelligent weather forecaster system) that integrates weather data obtained from existing massive sensing infrastructure and processes data using state-of-the-art communication and computational technologies. IrMaSet generates hyper-local real-time weather and forecast information (HyReF) and enables accurate estimation of the amount of electricity to be generated by VRES, resulting in the optimal management of microgrid energy systems. We present challenges and possible solutions for deploying IrMaSet and demonstrate the feasibility of IrMaSet in saving energy, and reducing costs and carbon emissions, using meteorological data including wind and solar radiation from eleven official monitoring stations (OMS) in greater Helsinki, Finland.",

keywords = "Computational modeling, Costs, Forecasting, Intelligent sensors, Meteorology, Predictive models, Weather forecasting",

author = "Zaidan, {Martha Arbayani} and Motlagh, {Naser Hossein} and Behnam Zakeri and Tuukka Petaja and Markku Kulmala and Sasu Tarkoma",

note = "Publisher Copyright: IEEE",

year = "2024",

doi = "10.1109/MCE.2024.3382438",

language = "English",

pages = "1--13",

journal = "IEEE Consumer Electronics Magazine",

issn = "2162-2248",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

Zaidan, MA, Motlagh, NH, Zakeri, B, Petaja, T, Kulmala, M & Tarkoma, S 2024, 'IrMaSet: Intelligent Weather Forecaster System for Hyper-Local Renewable Energies' IEEE Consumer Electronics Magazine, S. 1-13. https://doi.org/10.1109/MCE.2024.3382438

IrMaSet: Intelligent Weather Forecaster System for Hyper-Local Renewable Energies. / Zaidan, Martha Arbayani; Motlagh, Naser Hossein; Zakeri, Behnam et al.
in: IEEE Consumer Electronics Magazine, 2024, S. 1-13.

Publikation: Populärwissenschaftliche Artikel (z.B. Magazine) › Populärwissenschaftlicher Artikel

TY - GEN

T1 - IrMaSet

T2 - Intelligent Weather Forecaster System for Hyper-Local Renewable Energies

AU - Zaidan, Martha Arbayani

AU - Motlagh, Naser Hossein

AU - Zakeri, Behnam

AU - Petaja, Tuukka

AU - Kulmala, Markku

AU - Tarkoma, Sasu

N1 - Publisher Copyright:IEEE

PY - 2024

Y1 - 2024

N2 - Weather forecasting plays a vital role in estimating energy generation from variable renewable energy sources (VRES). Current weather forecast methods for estimating energy from VRES are typically at a scale of a few kilometers, which is not the fine spatial resolution needed for distributed energy planning. In this paper, we propose IrMaSet (an intelligent weather forecaster system) that integrates weather data obtained from existing massive sensing infrastructure and processes data using state-of-the-art communication and computational technologies. IrMaSet generates hyper-local real-time weather and forecast information (HyReF) and enables accurate estimation of the amount of electricity to be generated by VRES, resulting in the optimal management of microgrid energy systems. We present challenges and possible solutions for deploying IrMaSet and demonstrate the feasibility of IrMaSet in saving energy, and reducing costs and carbon emissions, using meteorological data including wind and solar radiation from eleven official monitoring stations (OMS) in greater Helsinki, Finland.

AB - Weather forecasting plays a vital role in estimating energy generation from variable renewable energy sources (VRES). Current weather forecast methods for estimating energy from VRES are typically at a scale of a few kilometers, which is not the fine spatial resolution needed for distributed energy planning. In this paper, we propose IrMaSet (an intelligent weather forecaster system) that integrates weather data obtained from existing massive sensing infrastructure and processes data using state-of-the-art communication and computational technologies. IrMaSet generates hyper-local real-time weather and forecast information (HyReF) and enables accurate estimation of the amount of electricity to be generated by VRES, resulting in the optimal management of microgrid energy systems. We present challenges and possible solutions for deploying IrMaSet and demonstrate the feasibility of IrMaSet in saving energy, and reducing costs and carbon emissions, using meteorological data including wind and solar radiation from eleven official monitoring stations (OMS) in greater Helsinki, Finland.

KW - Computational modeling

KW - Costs

KW - Forecasting

KW - Intelligent sensors

KW - Meteorology

KW - Predictive models

KW - Weather forecasting

UR - http://www.scopus.com/inward/record.url?scp=85189318198&partnerID=8YFLogxK

U2 - 10.1109/MCE.2024.3382438

DO - 10.1109/MCE.2024.3382438

M3 - Popular science article

AN - SCOPUS:85189318198

SN - 2162-2248

SP - 1

EP - 13

JO - IEEE Consumer Electronics Magazine

JF - IEEE Consumer Electronics Magazine

ER -

Zaidan MA, Motlagh NH, Zakeri B, Petaja T, Kulmala M, Tarkoma S. IrMaSet: Intelligent Weather Forecaster System for Hyper-Local Renewable Energies. IEEE Consumer Electronics Magazine. 2024;1-13. doi: 10.1109/MCE.2024.3382438