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Prediction Intelligent System In The Field Of Renewable Energies Through Neural Networks

Author

Listed:
  • Ion LUNGU
  • Adela BÂRA

    (The Bucharest Academy of Economic Studies)

  • George CĂRUTASU

    (The Romanian-American University)

  • Alexandru PÎRJAN,

    (The Romanian-American University)

  • Simona-Vasilica OPREA

    (The Bucharest Academy of Economic Studies)

Abstract

In this paper, we have developed a series of neural networks in order to design a decision support system for predicting, analysing and monitoring the performance indicators in the field of renewable energies in Romania. We have first analysed a series of comparative aspects regarding the algorithms used for developing the neural networks: the Levenberg-Marquardt, the Bayesian Regularization and the Scaled Conjugate Gradient algorithms. Then, we have developed, trained, validated and tested several neural networks based on the above-mentioned algorithms, using the Neural Network Toolbox from the development environment MatlabR2015a. Thus, we have obtained a solution that forecasts the total active energy export and the total active power, when knowing the solar irradiation, the ambient temperature, the humidity, the wind direction and the wind speed.

Suggested Citation

  • Ion LUNGU & Adela BÂRA & George CĂRUTASU & Alexandru PÎRJAN, & Simona-Vasilica OPREA, 2016. "Prediction Intelligent System In The Field Of Renewable Energies Through Neural Networks," ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, Faculty of Economic Cybernetics, Statistics and Informatics, vol. 50(1), pages 85-102.
    • Handle: RePEc:cys:ecocyb:v:50:y:2016:i:1:p:85-102
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    References listed on IDEAS

    as
    1. Adela Bara & Ion Lungu & Simona Vasilica Oprea & George Carutasu & Cornelia Paulina Botezatu & Cezar Botezatu, 2014. "Design Workflow For Cloud Service Information System For Integration And Knowledge Management Based In Renewable Energy," Romanian Economic Business Review, Romanian-American University, vol. 8(2), pages 230-237, December.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. George C?ru?a?u & Alexandru Pîrjan, 2016. "A Seasonal And Monthly Approach For Predicting The Delivered Energy Quantity In A Photovoltaic Power Plant In Romania," Annals of University of Craiova - Economic Sciences Series, University of Craiova, Faculty of Economics and Business Administration, vol. 1(44), pages 198-207.
    2. Cicerone Laurentiu Popa & George Carutasu & Costel Emil Cotet & Nicoleta Luminita Carutasu & Tiberiu Dobrescu, 2017. "Smart City Platform Development for an Automated Waste Collection System," Sustainability, MDPI, vol. 9(11), pages 1-15, November.
    3. Prince Waqas Khan & Yongjun Kim & Yung-Cheol Byun & Sang-Joon Lee, 2021. "Influencing Factors Evaluation of Machine Learning-Based Energy Consumption Prediction," Energies, MDPI, vol. 14(21), pages 1-22, November.
    4. Alexandru Pîrjan & George Căruțașu & Dana-Mihaela Petroșanu, 2018. "Designing, Developing, and Implementing a Forecasting Method for the Produced and Consumed Electricity in the Case of Small Wind Farms Situated on Quite Complex Hilly Terrain," Energies, MDPI, vol. 11(10), pages 1-42, October.
    5. Simona-Vasilica Oprea & Adela Bâra & Adina Ileana Uță & Alexandru Pîrjan & George Căruțașu, 2018. "Analyses of Distributed Generation and Storage Effect on the Electricity Consumption Curve in the Smart Grid Context," Sustainability, MDPI, vol. 10(7), pages 1-25, July.
    6. Cristian Mihai BARCA & Claudiu Dan BARCA, 2017. "Distributed algorithm to train neural networks using the Map Reduce paradigm," Database Systems Journal, Academy of Economic Studies - Bucharest, Romania, vol. 8(1), pages 3-11, July.
    7. Alexandru PIRJAN, 2017. "Solutions for Optimizing the Relational JOIN Operator using the Compute Unified Device Architecture," Database Systems Journal, Academy of Economic Studies - Bucharest, Romania, vol. 7(3), pages 3-13, January.
    8. Alexandru Pîrjan, 2016. "A Mixed Approach Towards Improving Software Performance Of Compute Unified Device Architecture Applications," Romanian Economic Business Review, Romanian-American University, vol. 10(2), pages 448-459, December.

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    1. Cornelia Paulina BOTEZATU & Cezar BOTEZATU & George CARUTASU & Alexandru PÎRJAN, 2015. "Research Issues Regarding The Main Indicators Used For Analysing The Incomes And Costs Of The Renewable Energy Producers Operating In Romania In View Of Developing A Decision Support System," Romanian Economic Business Review, Romanian-American University, vol. 9(1), pages 50-62, May.

    More about this item

    Keywords

    Neural Networks; algorithms; renewable energy; solar power plant; Decision Support System.;
    All these keywords.

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • L86 - Industrial Organization - - Industry Studies: Services - - - Information and Internet Services; Computer Software
    • O13 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Agriculture; Natural Resources; Environment; Other Primary Products
    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

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