THERMAL SCIENCE

International Scientific Journal

Authors of this Paper

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Anamarija Falkoni

,

Goran Krajačić

LINEAR CORRELATION AND REGRESSION BETWEEN THE METEOROLOGICAL DATA AND THE ELECTRICITY DEMAND OF THE DUBROVNIK REGION IN A SHORT-TERM SCALE

ABSTRACT
Intermitted patterns of solar and wind generation cause insecurity in the power system supply since they depend on weather conditions. The aim of this work is to perform linear regression and correlation between solar radiation, wind speed, air temperature and electricity demand of the Dubrovnik region. Gained results could help in system energy planning with a high share of renewable energy sources in the electricity production. All of the data are collected for consecutive three year period, years 2012, 2013 and 2014, in the 10 minute time step. Results of the correlation of each of the parameters individually in between the years show slightly variations between the distributions, providing representative line-ar relation in between the years for all the data, except the wind speed data. Cor-relations are also done between all of the parameters for each year separately, based on the mean monthly values. Result showed good relation with negative correlation between solar radiation and wind speed, as well as good relation with positive correlation between solar radiation and electricity demand. The same correlations are done in the 10 minute time step and including time system delay. The results indicate significant decrease in correlation coefficient value and it is less possible that they can be pronounced with linear regression line. Calculations of the correlation and regression, based on the 10 minute time step for summer and winter period separately, gained slightly better results in relation between parameters than the ones including the whole year data.
KEYWORDS
wind speed, solar radiation, air temperature, electricity demand, correlation coefficient
PAPER SUBMITTED: 2015-12-09
PAPER REVISED: 2016-04-25
PAPER ACCEPTED: 2016-04-26
PUBLISHED ONLINE: 2016-07-12
DOI REFERENCE: https://doi.org/10.2298/TSCI151209157F
CITATION EXPORT: view in browser or download as text file
THERMAL SCIENCE YEAR 2016, VOLUME 20, ISSUE Issue 4, PAGES [1073 - 1089]
REFERENCES
  1. ***, Direktiva 2009/30/EZ Europskog parlamenta i vijeća, 2009, eur-lex.europa.eu/legal-content/HR/TXT/?uri=celex:32009L0030
  2. ***, Energy Strategy of the Republic of Croatia, Zagreb, June 2009, weg.ge/wp-content/uploads/2013/05/Croatia-Energy-Strategy-2009.pdf
  3. Kayal, P., Chanda, C. K., A Multi-objective Approach to Integrate Solar and Wind Energy Sources with Electrical Distribution Network, Solar Energy, 112 (2015), pp. 397-410
  4. Tsekouras, G., Koutsoyiannis, D., Stochastic Analysis and Simulation of Hydrometeorological Process-es Associated with Wind and Solar Energy, Renewable Energy, 63 (2014), pp. 624-633
  5. Kayal, P., Chanda, C. K., Optimal Mix of Solar and Wind Distributed Generations Considering Perfor-mance Improvement of Electrical Distribution Network, Renewable Energy, 75 (2015), pp. 173-186
  6. Purvins, A., et al., Effects of Variable Renewable Power on a Country-scale Electricity System: High Penetration of Hydro Power Plants and Wind Farms in Electricity Generation, Energy, 43 (2012), pp. 225-236
  7. Silvente, J., et al., A Rolling Horizon Optimization Framework for the Simultaneous Energy Supply and Demand Planning in Microgrids, Applied Energy, 155 (2015), pp. 485-501
  8. Mahesh, A., Sandhu, K. S., Hybrid Wind/photovoltaic Energy System Developments: Critical Review and Findings, Renewable and Sustainable Energy Review, 52 (2015), pp. 1135-1147
  9. Pérez-Navarro, A., et al., Experimental Verification of Hybrid Renewable Systems as Feasible Energy Sources, Renewable Energy, 86 (2016), pp. 384-391
  10. Mesarić, P., Krajcar, S., Home Demand Side Management Integrated with Electric Vehicles and Re-newable Energy Sources, Energy and Buildings, 108 (2015), pp. 1-9
  11. Cai, Z., et al., Application of Battery Storage for Compensation of Forecast Errors of Wind Power Generation in 2050, Energy Procedia, 73 (2015), pp. 208-217
  12. Foley, A. M., et al., Current Methods and Advances in Forecasting of Wind Power Generation, Renew-able Energy, 37 (2012), 1, pp. 1-8
  13. Rasheed, A., et al., A Multiscale Wind and Power Forecast System for Wind Farms, Energy Procedia, 53 (2014), pp. 290-299
  14. Wang, X., et al., A Review of Wind Power Forecasting Models, Energy Procedia, 12 (2011), pp. 770-778
  15. De Giorgi, M. G., et al., Assessment of the Benefits of Numerical Weather Predictions in Wind Power Forecasting Based on Statistical Methods, Energy, 36 (2011), 7, pp. 3968-3978
  16. Zhao, X., et al., Review of Evaluation Criteria and Main Methods of Wind Power Forecasting, Energy Procedia, 12 (2011), pp. 761-769
  17. Alessandrini, S., et al., A Novel Application of an Analog Ensemble for Short-term Wind Power Fore-casting, Renewable Energy, 76 (2015), pp. 768-781
  18. Hong, Y. Y., et al., Hour-ahead Wind Power and Speed Forecasting Using Simultaneous Perturbation Stochastic Approximation (SPSA) Algorithm and Neural Network with Fuzzy Input, Energy, 35 (2010), 9, pp. 3870-3876
  19. Alonso-Montesinos, J., Batlles, F. J., Solar Radiation Forecasting in the Short- and Medium-term Under all Sky Conditions, Energy, 83 (2015), pp. 387-393
  20. Gupta, R. A., et al., BBO-based Small Autonomous Hybrid Power System Optimization Incorporating Wind Speed and Solar Radiation Forecasting, Renewable and Sustainable Energy Reviews, 41 (2015), pp. 1366-1375
  21. Tascikaraoglu, A., et al., An Adaptive Load Dispatching and Forecasting Strategy for a Virtual Power Plant Including Renewable Energy Conversion Units, Applied Energy, 119 (2014), pp. 445-453
  22. He, W., Deep Neural Network Based Load Forecast, Computer Modelling & New Technologies, 18 (2014), 3, pp. 258-262
  23. Monforti, F., et al., Assessing Complementarity of Wind and Solar Resources for Energy Production in Italy, A Monte Carlo Approach, Renewable Energy, 63 (2014), pp. 576-586
  24. Alham, M. H., et al., Optimal Operation of Power System Incorporating Wind Energy with Demand Side Management, Ain Shams Engineering Journal, (2015), Article in press
  25. Guan, H., et al., Response of Office Building Electricity Consumption to Urban Weather in Adelaide, South Australia, Urban Climate, 10 (2014), 1, pp. 42-55
  26. de Jong, P., et al., Solar and Wind Energy Production in Relation to the Electricity Load Curve and Hydroelectricity in the Northeast Region of Brazil, Renewable and Sustainable Energy Reviews, 23 (2013), pp. 526-535
  27. Huber, M., et al., Integration of Wind and Solar Power in Europe, Energy, 69 (2014), pp. 236-246
  28. Bale, C. S. E., et al., Energy and Complexity: New Ways Forward, Applied Energy, 138 (2015), pp. 150-159
  29. Foley, A. M., et al., Addressing the Technical and Market Challenges to High Wind Power Integration in Ireland, Renewable and Sustainable Energy Reviews, 19 (2013), pp. 692-703
  30. Jaradat, M., et al., The Internet of Energy: Smart Sensor Networks and Big Data Management for Smart Grid, Procedia Computer Science, 56 (2015), pp. 592-597
  31. Gaviano, A., et al., Challenges and Integration of PV and Wind Energy Facilities from a Smart Grid Point of View, Energy Procedia, 25 (2012), pp. 118-125
  32. Mourshed, M., et al., Smart Grid Futures: Perspectives on the Integration of Energy and ICT Services, Energy Procedia, 75 (2015), pp. 1132-1137
  33. Lund, H., et al., From Electricity Smart Grids to Smart Energy Systems ‒ A Market Operation Based Approach and Understanding, Energy, 42 (2012), 1, pp. 96-102
  34. Lund, H., et al., Renewable Energy Systems ‒ A Smart Energy Systems Approach to the Choice and Modelling of 100% Renewable Solutions, Chemical Engineering Transactions, 39 (2014), pp. 1-6 DOI No. 10.3303/CET1439001
  35. Šare, A., et al., The Integration of Renewable Energy Sources and Electric Vehicles Into the Power System of the Dubrovnik Region, Energy, Sustainability and Society, 5 (2015), 27, DOI No. 10.1186/s13705-015-0055-7
  36. ***, Meteorological and Hydrological Service, meteo.hr/index_en.php
  37. ***, www.kippzonen.com/Product/14/CMP21-Pyranometer#.VvUN59IrLIV
  38. ***, www.kippzonen.com/Product/262/CVF4-Ventilation-Unit#.VvUN6NIrLIV
  39. ***, www.kippzonen.com/Search.aspx?id=304&query=cm121#gsc.tab=0&gsc.q=cm121&gsc. page=1
  40. ***, Elektrojug Dubrovnik - HEP ODS d.o.o., www.hep.hr/ods/en/dp/default.aspx
  41. ***, STATISTICA, www.statsoft.com/
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Linear correlation and regression between the meteorological data and the electricity demand of the Dubrovnik region in a short-term scale

© 2024 Society of Thermal Engineers of Serbia. Published by the Vinča Institute of Nuclear Sciences, National Institute of the Republic of Serbia, Belgrade, Serbia. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International licence