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Climate Change and its Effect on the Energy Production from Renewable Sources – A Case Study in Mediterranean Region

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In terms of climate forecasting, the Mediterranean region is among the most difficult. It is correlated with the five significant subtropical high pressure belts of the oceans and is symbolized by dry and hot summer and cold and rainy winter. Due to its location in the area, Albania is particularly susceptible to climatic changes. It has been noted that summertime sees the greatest temperature increases. More intense heat waves that stay longer and occur more frequently are anticipated in the eastern Mediterranean. The seasonal patterns of precipitation have not changed, but the amount of rain has become more intense. The effects of climate change have drawn attention to various renewable energy sources, including solar and wind power. In this study, the changes and prospective in average temperature, rainfall, humidity, CO2 emission and their impact in energy production were investigated. Several different models such as Auto Regressive Integrated Moving Average method; Prophet algorithm; Elastic-Net Regularized Generalized Linear Model; Random Forest Regression models; Prophet Boost algorithm; have been built for the study and prediction of each variable. The appropriate models are used to determine the anticipated values of the indicators for a period of four years. The prediction shows an increase in CO2 emission which leads to a decrease in energy production by hydropower. These findings suggest the use of other renewable sources for energy production in the country and the Mediterranean region.
Rocznik
Strony
285--298
Opis fizyczny
Bibliogr. 22 poz., rys., tab.
Twórcy
autor
  • Department of Applied Mathematics, Faculty of Natural Science, University of Tirana, Sheshi Nënë Tereza 4, Tirana 1010, Albania
autor
  • Department of Applied Mathematics, Faculty of Natural Science, University of Tirana, Sheshi Nënë Tereza 4, Tirana 1010, Albania
  • Department of Applied Mathematics, Faculty of Natural Science, University of Tirana, Sheshi Nënë Tereza 4, Tirana 1010, Albania
Bibliografia
  • 1. Box G.E.P., Jenkins G.M. 1976. Time series analysis: forecasting and control. Revised Edition, Holden-Day, San Francisco, CA.
  • 2. Breiman L. 2001. Random Forests. Machine Learning, 45, 5–32. DOI: 10.1023/A:1010933404324
  • 3. Emodi N.V., Chaiechi T., Alam Beg A.R. 2018. The impact of climate change on electricity demand in Australia. Energy & Environment, 29(7), 1263–1297. DOI: 10.1177/0958305X18776538
  • 4. Eysenbach J., Franklin B., Larsen A.J., Lindsey J. 2021. Predicting Power Using Time Series Analysis of Power Generation and Consumption in Texas. SMU Data Science Review, 5(3). https://scholar.smu.edu/datasciencereview/vol5/iss3/5
  • 5. Franco G., Sanstad A.H. 2008. Climate change and electricity demand in California. Climatic Change, 87(1), 139–151, https://doi.org/10.1007/s10584-007-9364-y
  • 6. Friedma J., Hastie T., Tibshirani R. 2010. Regularization Paths for Generalized Linear Models via Coordinate Descent. Journal of Statistical Software, 33(1), 1–22 https://doi.org/10.18637/jss.v033.i01
  • 7. Gjika E., Ferrja A., Kamberi A. 2019. A study on the efficiency of hybrid models in forecasting precipitations and water inflow Albania case study. Advances in Science, Technology and Engineering Systems, 4(1), 302–310. DOI: 10.25046/aj040129
  • 8. Gjika E., Basha L., Ferrja A., Kamberi A. 2021. Analyzing Seasonality in Hydropower Plants Energy Production and External Variables. Eng. Proc., 5(15). DOI: 10.3390/engproc2021005015
  • 9. Gjika E., Basha L. 2022. A Comparative Study of Statistical and Deep Learning Models Used in Energy Load Prediction. World Conference on Sustainability, Energy and Environment. Paris, France.
  • 10. Guo L.N., She C., Kong D.B., Yan S.L., Xu Y.P., Khayatnezhad M., Gholinia F. 2021. Prediction of the effects of climate change on hydroelectric generation, electricity demand, and emissions of greenhouse gases under climatic scenarios and optimized ANN model, Energy Reports, 7, 5431–5445. DOI: 10.1016/j.egyr.2021.08.134
  • 11. Hale J., Long S. 2021. A Time Series Sustainability Assessment of a Partial Energy Portfolio Transition. Energies, 14(1), 141. DOI: 10.3390/en14010141
  • 12. Harris R., Sollis R. 2003. Applied Time Series Modelling and Forecasting. Hoboken, NJ: John Wiley and Sons.
  • 13. Hyndman R.J., Koehler A.B. 2006. Another look at measures of forecast accuracy, International Journal of Forecasting, 22(4), 679–688. DOI: 10.1016/j.ijforecast.2006.03.001.
  • 14. Lahouar A., Ben Hadj Slama J. 2015. Random forests model for one day ahead load forecasting. IREC2015 The Sixth International Renewable Energy Congress, 1–6, DOI: 10.1109/IREC.2015.7110975
  • 15. Mason K., Duggan J., Howley E. 2018. Forecasting energy demand, wind generation and carbon dioxide emissions in Ireland using evolutionary neural networks, Energy, 155, 705–720. DOI: 10.1016/j.energy.2018.04.192
  • 16. Meng M., Song C. 2020. Daily Photovoltaic Power Generation Forecasting Model Based on Random Forest Algorithm for North China in Winter. Sustainability, 12(6), 2247. DOI: 10.3390/su12062247
  • 17. Serras P., Ibarra-Berastegi G., Sáenz J., Ulazia A. 2019. Combining random forests and physics-based models to forecast the electricity generated by ocean waves: A case study of the Mutriku wave farm, Ocean Engineering, 189, 106314. DOI: 10.1016/j.oceaneng.2019.106314
  • 18. Tay J.K., Narasimhan B., Hastie T. 2021. Elastic Net Regularization Paths for All Generalized Linear Models. DOI: 10.48550/arXiv.2103.03475
  • 19. Taylor S.J., Letham B. 2018. Forecasting at scale. The American Statistician, 72(1), 37–45, DOI: 10.1080/00031305.2017.1380080
  • 20. Wadsack K., Acker T.L. 2019. Climate change and future power systems: the importance of energy storage in reduced-hydropower systems in the American Southwest, Clean Energy, 3(4), 241–250. DOI: 10.1093/ce/zkz018
  • 21. Zhang C., Liao H., Mi Z. 2019. Climate impacts: temperature and electricity consumption. Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer. International Society for the Prevention and Mitigation of Natural Hazards, 99(3), 1259–1275, https://doi.org/10.1007/s11069-019-03653-w
  • 22. The World Bank, https//www.worldbank.org
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-6ceb9e83-e4a4-41c5-954a-3a9d4ee750d2
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