Ước lượng bức xạ mặt trời bằng cách áp dụng đồng thời tái cấu trúc không gian pha và mô hình mạng nơ-ron lai

Springer Science and Business Media LLC - Tập 147 - Trang 1725-1742 - 2022
Mahsa H. Kashani1, Samed Inyurt2, Mohammad Reza Golabi3, Mohammad AmirRahmani4, Shahab S. Band5
1Department of Water Engineering, Water Management Research Center, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran
2Department of Geomatics Engineering, Faculty of Engineering and Architecture, Tokat Gaziosmanpasa University, Tokat, Turkey
3Faculty of Water Sciences Engineering, University of Shahid Chamran Ahvaz, Ahvaz, Iran
4Institute of the Environment, University of Tabriz, Tabriz, Iran
5Future Technology Research Center, College of Future, National Yunlin University of Science and Technology, Douliou, Taiwan, ROC

Tóm tắt

Việc ước lượng bức xạ mặt trời có thể đóng vai trò then chốt trong quản lý môi trường cũng như các lĩnh vực khác như năng lượng, nông nghiệp, và mô hình thủy văn, sinh thái. Tại một số khu vực, dữ liệu bức xạ mặt trời không đủ do thiếu thiết bị pyranometer hoặc thiết bị này thường xuyên bị hỏng. Do đó, việc có một bộ ước lượng để ước lượng bức xạ mặt trời dựa trên các biến khí hậu khác là rất quan trọng. Để phát triển một công cụ ước lượng, hai mô hình đã được áp dụng đồng thời như một mô hình lai mới để ước lượng bức xạ mặt trời toàn cầu hàng tháng cho ba khu vực ở Iran, làm nghiên cứu điển hình của công trình nghiên cứu này: (1) một mạng nơ-ron nhân tạo (ANN) được tối ưu hóa bằng thuật toán tối ưu hóa của chim diều hâu Harris (HHO) (ANNHHO) và (2) tái cấu trúc không gian pha (PSR) tích hợp với mô hình lai ANNHHO (PSR-ANNHHO). Dữ liệu khí tượng hàng tháng về nhiệt độ tối thiểu (Tmin), nhiệt độ tối đa (Tmax), nhiệt độ trung bình (Tmean), số giờ nắng (SH), tốc độ gió (U2) và độ ẩm tương đối (RH) trong 37 năm (1985–2018) từ ba khu vực ở Iran với các loại khí hậu khác nhau đã được sử dụng để đào tạo và kiểm tra các mô hình đã phát triển. Để chọn các biến đầu vào phù hợp cho các mô hình, một thuật toán relief đã được áp dụng. Hiệu suất của các mô hình lai mới được so sánh với mô hình ANN độc lập. Kết quả thu được cho thấy mặc dù tất cả các mô hình thông minh hoạt động thoả đáng, mô hình lai PSR-ANNHHO vượt trội so với mô hình lai ANNHHO và mô hình ANN độc lập ở tất cả các khu vực. Mô hình ANNHHO lai theo sau mô hình PSR-ANNHHO như một mô hình chính xác thứ hai.

Từ khóa

#bức xạ mặt trời #mô hình mạng nơ-ron nhân tạo #tối ưu hóa #tái cấu trúc không gian pha #khí tượng học #mô hình lai

Tài liệu tham khảo

Abarbanel HDI (1996) Choosing the dimension of reconstructed phase space. In: Analysis of observed chaotic data. Institute for nonlinear science. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-0763-4_4 Al-Alawi SM, Al-Hinai HA (1998) An ANN-based approach for predicting global radiation in locations with no direct measurement instrumentation. Renew Energy 14(1–4):199–204 Almaraashi M (2018) Investigating the impact of feature selection on the prediction of solar radiation in different locations in Saudi Arabia. Appl Soft Comput 66:250–263 Basaran K, Özçift A, Kılınç D (2019) A new approach for prediction of solar radiation with using ensemble learning algorithm. Arab J Sci Eng. https://doi.org/10.1007/s13369-019-03841-7 Baydaroǧlu Ö, Koçak K (2014) SVR-based prediction of evaporation combined with chaotic approach. J Hydrol 508:356–363. https://doi.org/10.1016/j.jhydrol.2013.11.008 Behrang MA, Assareh E, Ghanbarzadeh A, Noghrehabadi AR (2010) The potential of different artificial neural network (ANN) techniques in daily global solar radiation modeling based on meteorological data. Sol Energy 84(8):1468–1480 Benghanem M, Mellit A, Alamri SN (2009) ANN-based modelling and estimation of daily global solar radiation data: A case study. Energy Convers Manage 50(7):1644–1655 Casdagli M (1989) Nonlinear prediction of chaotic time series. Phys D Nonlin Phenom 35:335–356 Dhanya CT, Kumar DN (2010) Nonlinear ensemble prediction of chaotic daily rainfall. Adv Water Resour 33:327–347 Dhanya CT, Kumar DN (2011) Multivariate nonlinear ensemble prediction of daily chaotic rainfall with climate inputs. J Hydrol 403:292–306 Elshorbagy A, Simonovic SP, Panu US (2002) Estimation of missing streamflow data using principles of chaos theory. J Hydrol 255:123–133. https://doi.org/10.1016/S0022-1694(01)00513-3 Farmer JD, Sidorowich JJ (1987) Predicting chaotic time series. Phys Rev Lett 59:845 Fraser AM, Swinney HL (1986) Independent coordinates for strange attractors from mutual information. Phys Rev A 33:1134–1140. https://doi.org/10.1103/PhysRevA.33.1134 Fathima TA, Nedumpozhimana V, Lee YH, Winkler S, Dev S (2019) A chaotic approach on solar irradiance forecasting. Photonics & Electromagnetics Research Symposium - fall (PIERS - Fall), Xiamen, China 2724–2728, https://doi.org/10.1109/PIERS-Fall48861.2019.9021305 Gaume E, Sivakumar B, Kolasinski M, Hazoumé L (2006) Identification of chaos in rainfall temporal disaggregation: application of the correlation dimension method to 5-minute point rainfall series measured with a tipping bucket and an optical raingage. J Hydrol 328:56–64 Ghorbani MA, Khatibi R, Mehr AD, Asadi H (2018) Chaos-based multigene genetic programming: a new hybrid strategy for river flow forecasting. J Hydrol 562:455–467 Golder J, Joelson M, Neel MC, Di Pietro L (2014) A time fractional model to represent rainfall process. Water Sci Eng 7:32–40 Guermoui M, Gairaa K, Boland J, Arrif T (2021) A novel hybrid model for solar radiation forecasting using support vector machine and bee colony optimization algorithm: review and case study. J Solar Energy Eng 143(2):020801 Halabi LM, Mekhilef S, Hossain M (2018) Performance evaluation of hybrid adaptive neuro-fuzzy inference system models for predicting monthly global solar radiation. Appl Energy 213:247–261. https://doi.org/10.1016/j.apenergy.2018.01.035 Haykin S (2009) Neural Networks and Learning Machines, 3rd edn. McMaster University, Canada Heidari AA, Mirjalili S, Farisetal H (2019) Harris hawks’ optimization: algorithm and applications. Future Gener Comput Syst. https://doi.org/10.1016/j.future.2019.02 Holzfuss J, Mayer-Kress G (1986) An approach to error-estimation in the application of dimension algorithms, in: Dimensions and Entropies in Chaotic Systems. Springer, 114–122 Huang SC, Chuang PJ, Wu CF, Lai HJ (2010) Chaos-based support vector regressions for exchange rate forecasting. Expert Syst Appl 37:8590–8598 Ibrahim IA, Khatib T (2017) A novel hybrid model for hourly global solar radiation prediction using random forests technique and firefly algorithm. Energy Convers Manage 138:413–425. https://doi.org/10.1016/j.enconman.2017.02.006 Jadidi A, Menezes R, de Souza N, de Castro LA (2018) A hybrid GA–MLPNN model for one-hour-ahead forecasting of the global horizontal irradiance in Elizabeth city, North Carolina. Energies 11(10):2641. https://doi.org/10.3390/en11102641 Kalogirou SA (2001) Artificial neural networks in renewable energy systems applications: A review. Renew Sust Energ Rev 5(4):373–401 Kashani MH, Ghorbani MA, Shahabi M, Naganna SR, Diop L (2020) Multiple AI model integration strategy - application to saturated hydraulic conductivity prediction from easily available soil properties. Soil Tillage Res 196:104449 Kennel MB, Brown R, Abarbanel HDI (1992) Determining embedding dimension for phase-space reconstruction using a geometrical construction. Phys Rev A 45:3403–3411. https://doi.org/10.1103/PhysRevA.45.3403 Khatibi R, Sivakumar B, Ghorbani MA, Kisi O, Koçak K, Farsadi Zadeh D (2012) Investigating chaos in river stage and discharge time series. J Hydrol 414–415:108–117. https://doi.org/10.1016/j.jhydrol.2011.10.026 Kira K, Rendell LA (1992) The feature selection problem: traditional methods and a new algorithm. AAAI-92 Proceedings of the tenth national conference on Artificial intelligence, 129–134 Koca A, Oztop HF, Varol Y, Koca GO (2011) Estimation of solar radiation using artificial neural networks with different input parameters for Mediterranean region of Anatolia in Turkey. Expert Syst Appl 38(7):8756–8762 Koçak K, Şaylan L, Eitzinger J (2004) Nonlinear prediction of near-surface temperature via univariate and multivariate time series embedding. Ecol Modell 173:1–7. https://doi.org/10.1016/S0304-3800(03)00249-7 Koutsoyiannis D, Pachakis D (1996) Deterministic chaos versus stochasticity in analysis and modeling of point rainfall series. J Geophys Res Atmos 101:26441–26451 Liebert W, Schuster HG (1989) Proper choice of the time delay for the analysis of chaotic time series. Phys Lett A 142:107–111 Lovejoy S, Mandelbrot BB (1985) Fractal properties of rain, and a fractal model. Tellus A 37:209–232 Malik A, Kumar A, Kim S, Kashani MH, Karimi V, Ghorbani MA, Al-Ansari N, Salih SQ, Yaseen ZM, Chau KW (2020) Modeling monthly pan evaporation process over the Indian central Himalayas: application of multiple learning artificial intelligence models. Eng Appl Comput Fluid Mech 14(1):323–338 McClelland JL, Rumelhart DE (1989) Explorations in parallel distributed processing: A handbook of models, programs, and exercises. MIT Press, Cambridge MA Mellit A (2008) Artificial Intelligence technique for modeling and forecasting of solar radiation data: A review. J Artif Intell Soft Comput Res 1(1):52–76 Moghaddamnia A, Remesan R, Kashani MH, Mohammadi M, Han D, Piri J (2009) Comparison of LLR, MLP, Elman, NNARX and ANFIS Models—with a case study in solar radiation estimation. J Atmos Solar-Terres Physics 71(8–9):975–982 Mohammadi K, Shamshirband S, Danesh AS, Zamani M, Sudheer C (2015) Horizontal global solar radiation estimation using hybrid SVM-firefly and SVM-wavelet algorithms: a case study. Natural Hazardshttps://doi.org/10.1007/s11069-015-2047-5 Mohanty S (2014) ANFIS based prediction of monthly average global solar radiation over Bhubaneswar (State of Odisha). Int J Ethics Eng Manage Edu 1(5):2348–4748 Mohanty S, Patra PK, Sahoo SS (2016) Prediction and application of solar radiation with soft computing over traditional and conventional approach—a comprehensive review. Renewable Sustain Energy Rev 56:778–796 Moreno A, Gilabert MA, Martı´nez B (2011) Mapping daily global solar irradiation over Spain: A comparative studyof selected approaches. Sol Energy 85(9):2072–2084 Ng WW, Panu US, Lennox WC (2007) Chaos based analytical techniques for daily extreme hydrological observations. J Hydrol 342:17–41. https://doi.org/10.1016/j.jhydrol.2007.04.023 Nourani V, Elkiran G, Abdullahi J, Tahsin A (2019) Multi-region modeling of daily global solar radiation with artificial intelligence ensemble. Natural Resour Res. https://doi.org/10.1007/s11053-018-09450-9 Olsson J, Niemczynowicz J, Berndtsson R (1993) Fractal analysis of high-resolution rainfall time series. J Geophys Res Atmos 98:23265–23274 Pasternack GB (1999) Does the river run wild? Assessing chaos in hydrological systems. Adv Water Resour 23:253–260 Porporato A, Ridolfi L (1996) Clues to the existence of deterministic chaos in river flow. Int J Mod Phys B 10:1821–1862 Porporato A, Ridolfi L (1997) Nonlinear analysis of river flow time sequences. Water Resour Res 33:1353–1367. https://doi.org/10.1029/96WR03535 Rahimikhoob A (2010) Estimating global solar radiation using artificial neural network and air temperature data in a semiarid environment. Renewable Energy 35(9):2131–2135 Rehman S, Mohandes M (2008) Artificial neural network estimation of global solar radiation using air temperature and relative humidity. Energy Policy 36(2):571–576 Rodriguez-Iturbe I, Febres De Power B, Sharifi MB, Georgakakos KP (1989) Chaos in rainfall. Water Resour Res 25:1667–1675 Rohani A, Taki M, Abdollahpour M (2018) A novel soft computing model (Gaussian process regression with K-fold cross validation) for daily and monthly solar radiation forecasting (Part: I). Renew Energy 115:411–422 Sammen SS, Ghorbani MA, Malik A, Tikhamarine Y, AmirRahmani M, Al-Ansari N, Chau KW (2020) Enhanced artificial neural network with harris hawks optimization for predicting scour depth downstream of ski-jump spillway. Appl Sci 10(15):5160 Shang P, Na X, Kamae S (2009) Chaotic analysis of time series in the sediment transport phenomenon. Chaos Soli Fractals 41:368–379 Sharifi SS, Rezaverdinejad V, Nourani V (2016) Estimation of daily global solar radiation using wavelet regression, ANN, GEP and empirical models: a comparative study of selected temperature-based approaches. J Atmos Solar-Terres. Physics. https://doi.org/10.1016/j.jastp.2016.10.008 Sivakumar B, Liong SY, Liaw CY (1998) Evidence of chaotic behavior in Singapore rainfall. J Am Water Resour Assoc 34:301–310 Sivakumar B, Liong SY, Liaw CY, Phoon KK (1999) Singapore rainfall behavior: chaotic? J Hydrol Eng 4:38–48 Sivakumar B (2000) Chaos theory in hydrology: important issues and interpretations. J Hydrol 227:1–20 Sivakumar B (2001) Rainfall dynamics at different temporal scales: a chaotic perspective. Hydrol Earth Syst Sci Discuss 5:645–652 Sivakumar B, Jayawardena AW (2002) An investigation of the presence of low-dimensional chaotic behaviour inthe sediment transport phenomenon. Hydrol Sci J 47:405–416. https://doi.org/10.1080/02626660209492943 Sun Y, Babovic V, Chan ES (2010) Multi-step-ahead model error prediction using time-delay neural networks combined with chaos theory. J Hydrol 395:109–116. https://doi.org/10.1016/j.jhydrol.2010.10.020 Takens F (1981) Detecting strange attractors in turbulence. In: Rand D, Young L-S (eds) Dynamical systems and turbulence, Warwick, 1980: Proceedings of a Symposium Held at the University of Warwick 1979/80. Springer Berlin Heidelberg, Berlin, Heidelberg, pp 366–381 Urbanowicz RJ, Meeker M, Cava WL, Olson RS, Moore JH (2018) Relief-based feature selection: Introduction and review. J Biomed Inform 85:189–203. https://doi.org/10.1016/j.jbi.2018.07.014 Uyumaz A, Danandeh Mehr A, Kahya E, Erdem H (2014) Rectangular side weirs discharge coefficient estimation in circular channels using linear genetic programming approach. J Hydroinform 16:1318–1330. https://doi.org/10.2166/hydro.2014.112 Wang Q, Gan TY (1998) Biases of correlation dimension estimates of streamflow data in the Canadian prairies. Water Resour Res 34:2329–2339 Yacef R, Mellit A, Belaid S, Şen Z (2014) New combined models for estimating daily global solar radiation from measured air temperature in semi-arid climates: application in Ghardaïa, Algeria. Energy Convers Manag 79:606–615. https://doi.org/10.1016/j.enconman.2013.12.057 Yadav AK, Chandel SS (2014) Solar radiation prediction using artificial neural network techniques: A review. Renew Sust Energ Rev 33:772–781