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Numerical Weather Prediction Data Free Solar Power Forecasting with Neural Networks

Published: 12 June 2018 Publication History

Abstract

The worldwide increase in renewable energy penetration levels has made accuracy, availability, and affordability of wind and solar energy forecasting systems an integral part of the modern power grids. The present paper describes an approach to forecasting one-day-ahead photovoltaic (PV) power generation without the use of numerical weather prediction (NWP) data. The presented approach uses a closed loop non-linear autoregressive artificial neural network (CL-NAR-ANN) model with only the historical generated PV power data as input. In case of emergency, if the communication channel with the weather provider fails, the whole forecasting system runs a risk of failing. Also, purchasing NWP data might be too expensive for smaller utilities. In such situations, NWP data free models can provide cost-effective and reasonably accurate PV power forecasts, which can act as a good backup solution. Furthermore, the model is evaluated using a dataset from the Global Energy Forecasting Competition of 2014 (GEFCom14) and its results are compared to other data-driven models such as polynomial and artificial neural network (ANN) models with and without NWP data as input. The results suggest that the CL-NAR-ANN model delivers acceptable forecasts and outperforms other NWP free models by a margin of 8% in terms of root mean square error, hence supporting the possibility of obtaining acceptable forecasts using the CL-NAR-ANN.

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  • (2024)Forecasting Solar Photovoltaic Power Production: A Comprehensive Review and Innovative Data-Driven Modeling FrameworkEnergies10.3390/en1716414517:16(4145)Online publication date: 20-Aug-2024
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    cover image ACM Conferences
    e-Energy '18: Proceedings of the Ninth International Conference on Future Energy Systems
    June 2018
    657 pages
    ISBN:9781450357678
    DOI:10.1145/3208903
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 12 June 2018

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    Author Tags

    1. Artificial Neural Networks
    2. Forecasting
    3. NAR
    4. NARX
    5. Photovoltaics
    6. Weather Free Forecast

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    • (2022)Assessing stacked physics-informed machine learning models for co-located wind–solar power forecastingSustainable Energy, Grids and Networks10.1016/j.segan.2022.10094332(100943)Online publication date: Dec-2022
    • (2021)Use of Forecasting in Energy Storage Applications: A ReviewIEEE Access10.1109/ACCESS.2021.31038449(114690-114704)Online publication date: 2021
    • (2021)DLT-based equity crowdfunding on the techno-economic feasibility of solar energy investmentsSolar Energy10.1016/j.solener.2021.08.067227(137-150)Online publication date: Oct-2021
    • (2020)An Enhanced Multiple Linear Regression Model for Seasonal Rainfall PredictionInternational Journal of Sensors, Wireless Communications and Control10.2174/221032791066619121812435010:4(473-483)Online publication date: 18-Dec-2020
    • (2020)Short-Term Predictive Power Management of PV-Powered NanogridsIEEE Access10.1109/ACCESS.2020.30152438(147839-147857)Online publication date: 2020
    • (2020)Practical Considerations For Customer-sited Energy Storage Dispatch On Multiple Applications Using Model Predictive ControlIFAC-PapersOnLine10.1016/j.ifacol.2020.12.133253:2(12465-12470)Online publication date: 2020
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