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Neural Computing and Applications

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19.04.2021 | Review Article

Boosting algorithms in energy research: a systematic review

verfasst von: Hristos Tyralis, Georgia Papacharalampous

Erschienen in: Neural Computing and Applications | Ausgabe 21/2021

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Abstract

Machine learning algorithms have been extensively exploited in energy research, due to their flexibility, automation and ability to handle big data. Among the most prominent machine learning algorithms are the boosting ones, which are known to be “garnering wisdom from a council of fools”, thereby transforming weak learners to strong learners. Boosting algorithms are characterized by both high flexibility and high interpretability. The latter property is the result of recent developments by the statistical community. In this work, we provide understanding on the properties of boosting algorithms to facilitate a better exploitation of their strengths in energy research. In this respect, (a) we summarize recent advances on boosting algorithms, (b) we review relevant applications in energy research with those focusing on renewable energy (in particular those focusing on wind energy and solar energy) consisting a significant portion of the total ones, and (c) we describe how boosting algorithms are implemented and how their use is related to their properties. We show that boosting has been underexploited so far, while great advances in the energy field are possible both in terms of explanation and interpretation, and in terms of predictive performance.

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Aler R, Galván IM, Ruiz-Arias JA, Gueymard CA (2017) Improving the separation of direct and diffuse solar radiation components using machine learning by gradient boosting. Sol Energy 150:558–569. https://doi.org/10.1016/j.solener.2017.05.018CrossRef

Andrade JR, Bessa RJ (2017) Improving renewable energy forecasting with a grid of numerical weather predictions. IEEE Trans Sustain Energy 8(4):1571–1580. https://doi.org/10.1109/TSTE.2017.2694340CrossRef

Andrade JR, Filipe J, Reis M, Bessa RJ (2017) Probabilistic price forecasting for day-ahead and intraday markets: beyond the statistical model. Sustainability 9(11):1990. https://doi.org/10.3390/su9111990CrossRef

Avila NF, Figueroa G, Chu C-C (2018) NTL detection in electric distribution systems using the maximal overlap discrete wavelet-packet transform and random undersampling boosting. IEEE Trans Power Syst 33(6):7171–7180. https://doi.org/10.1109/TPWRS.2018.2853162CrossRef

Bakker K, Whan K, Knap W, Schmeits M (2019) Comparison of statistical post-processing methods for probabilistic NWP forecasts of solar radiation. Sol Energy 191:138–150. https://doi.org/10.1016/j.solener.2019.08.044CrossRef

Beckel C, Sadamori L, Staake T, Santini S (2014) Revealing household characteristics from smart meter data. Energy 78:397–410. https://doi.org/10.1016/j.energy.2014.10.025CrossRef

Bessa RJ, Trindade A, Silva CSP, Miranda V (2015) Probabilistic solar power forecasting in smart grids using distributed information. Int J Electr Power Energy Syst 72:16–23. https://doi.org/10.1016/j.ijepes.2015.02.006CrossRef

Biau G, Cadre B, Rouvìère L (2019) Accelerated gradient boosting. Mach Learn 108(6):971–992. https://doi.org/10.1007/s10994-019-05787-1MathSciNetCrossRefMATH

Bickel PJ, Ritov Y, Zakai A (2006) Some theory for generalized boosting algorithms. J Mach Learn Res 7:705–732MathSciNetMATH

10.

Bogner K, Pappenberger F, Zappa M (2019) Machine learning techniques for predicting the energy consumption/production and its uncertainties driven by meteorological observations and forecasts. Sustainability 11(12):3328. https://doi.org/10.3390/su10023328CrossRef

11.

Boulesteix AL, Janitza S, Hapfelmeier A, Van Steen K, Strobl C (2015) Letter to the Editor: on the term ‘interaction’ and related phrases in the literature on random forests. Brief Bioinform 16(2):338–345. https://doi.org/10.1093/bib/bbu012CrossRef

12.

Breiman L (1998) Arcing classifiers. Ann Stat 26(3):801–849. https://doi.org/10.1214/aos/1024691079MathSciNetCrossRefMATH

13.

Breiman L (2001) Statistical modeling: the two cultures. Stat Sci 16(3):199–231. https://doi.org/10.1214/ss/1009213726MathSciNetCrossRefMATH

14.

Bühlmann P (2003) Boosting methods: why they can be useful for high-dimensional data. In: Proceedings of the 3rd International Workshop on Distributed Statistical Computing (DSC 2003)

15.

Bühlmann P (2006) Boosting for high-dimensional linear models. Ann Stat 34(2):559–583. https://doi.org/10.1214/009053606000000092MathSciNetCrossRefMATH

16.

Bühlmann P, Hothorn T (2007) Boosting algorithms: regularization, prediction and model fitting. Stat Sci 22(4):477–505. https://doi.org/10.1214/07-STS242MathSciNetCrossRefMATH

17.

Bühlmann P, Hothorn T (2007) Rejoinder: boosting algorithms: regularization, prediction and model fitting. Stat Sci 22(4):516–522. https://doi.org/10.1214/07-STS242REJCrossRefMATH

18.

Bühlmann P, Yu B (2003) Boosting with the L₂ loss. J Am Stat Assoc 98(462):324–339. https://doi.org/10.1198/016214503000125CrossRefMATH

19.

Bühlmann P, Yu B (2010) Boosting. Wiley Interdiscip Rev Comput Stat 2(1):69–74. https://doi.org/10.1002/wics.55CrossRef

20.

Buja A, Mease D, Wyner AJ (2007) Comment: boosting algorithms: regularization, prediction and model fitting. Stat Sci 22(4):506–512. https://doi.org/10.1214/07-STS242BCrossRefMATH

21.

Chen T, Guestrin C (2016) XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 785–794 doi: https://doi.org/10.1145/2939672.2939785

22.

Cheng L, Yu T (2019) A new generation of AI: a review and perspective on machine learning technologies applied to smart energy and electric power systems. Int J Energy Res 43(6):1928–1973. https://doi.org/10.1002/er.4333CrossRef

23.

Christidis P, Focas C (2019) Factors affecting the uptake of hybrid and electric vehicles in the European Union. Energies 12(18):3414. https://doi.org/10.3390/en12183414CrossRef

24.

Correa-Baena JP, Hippalgaonkar K, van Duren J, Jaffer S, Chandrasekhar VR, Stevanovic V, Wadia C, Guha S, Buonassisi T (2018) Accelerating materials development via automation, machine learning, and high-performance computing. Joule 2(8):1410–1420. https://doi.org/10.1016/j.joule.2018.05.009CrossRef

25.

Cui B, Fan C, Munk J, Mao N, Xiao F, Dong J, Kuruganti T (2019) A hybrid building thermal modeling approach for predicting temperatures in typical, detached, two-story houses. Appl Energy 236:101–116. https://doi.org/10.1016/j.apenergy.2018.11.077CrossRef

26.

Divina F, Gilson A, Goméz-Vela F, Torres MG, Torres JF (2018) Stacking ensemble learning for short-term electricity consumption forecasting. Energies 11(4):949. https://doi.org/10.3390/en11040949CrossRef

27.

Domingueza C, Orehounig K, Carmeliet J (2019) Modelling of rural electrical appliances ownership in developing countries to project their electricity demand: a case study of sub-Saharan Africa. Int J Sustain Energy Plan Manag 22:5–16. https://doi.org/10.5278/ijsepm.2564CrossRef

28.

Efron B, Hastie T (2016) Computer age statistical inference. Cambridge University Press, New YorkCrossRef

29.

Fan C, Xiao F, Yan C, Liu C, Li Z, Wang J (2019) A novel methodology to explain and evaluate data-driven building energy performance models based on interpretable machine learning. Appl Energy 235:1551–1560. https://doi.org/10.1016/j.apenergy.2018.11.081CrossRef

30.

Filipe J, Bessa RJ, Reis M, Alves R, Póvoa P (2019) Data-driven predictive energy optimization in a wastewater pumping station. Appl Energy 252:113423. https://doi.org/10.1016/j.apenergy.2019.113423CrossRef

31.

Foley AM, Leahy PG, Marvuglia A, McKeogh EJ (2012) Current methods and advances in forecasting of wind power generation. Renew Energy 37(1):1–8. https://doi.org/10.1016/j.renene.2011.05.033CrossRef

32.

Freund Y (1995) Boosting a weak learning algorithm by majority. Inf Comput 121(2):256–285. https://doi.org/10.1006/inco.1995.1136MathSciNetCrossRefMATH

33.

Freund Y, Schapire RE (1995) A desicion-theoretic generalization of on-line learning and an application to boosting. In: Vitányi P (ed) Computational Learning Theory EuroCOLT Lecture Notes in Computer Science Lecture Notes in Artificial Intelligence, vol 904. Springer, Berlin Heidelberg, pp 23–27

34.

Freund Y, Schapire RE (1996) Experiments with a new boosting algorithm. In: Proceedings of the Thirteenth International Conference on Machine Learning 148–156

35.

Freund Y, Schapire RE (1997) A decision-theoretic generalization of on-line learning and an application to boosting. J Comput Syst Sci 55(1):119–139. https://doi.org/10.1006/jcss.1997.1504MathSciNetCrossRefMATH

36.

Friedman JH (2001) Greedy function approximation: a gradient boosting machine. Ann Stat 29(5):1189–1232. https://doi.org/10.1214/aos/1013203451MathSciNetCrossRefMATH

37.

Friedman JH (2002) Stochastic gradient boosting. Comput Stat Data Anal 38(4):367–378. https://doi.org/10.1016/S0167-9473(01)00065-2MathSciNetCrossRefMATH

38.

Friedman JH, Hastie T, Tibshirani R (2000) Additive logistic regression: a statistical view of boosting. Ann Stat 28(2):337–407. https://doi.org/10.1214/aos/1016218223MathSciNetCrossRefMATH

39.

Ghimire S, Deo RC, Raj N, Mi J (2019) Deep learning neural networks trained with MODIS satellite-derived predictors for long-term global solar radiation prediction. Energies 12(12):2407. https://doi.org/10.3390/en12122407CrossRef

40.

Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning. Springer, New York, NYCrossRef

41.

Helbing G, Ritter M (2018) Deep learning for fault detection in wind turbines. Renew Sustain Energy Rev 98:189–198. https://doi.org/10.1016/j.rser.2018.09.012CrossRef

42.

Hoffmann F, Bertram T, Mikut R, Reischl M, Nelles O (2019) Benchmarking in classification and regression. Wiley Interdiscip Rev Data Min Knowl Discov 9(5):e1318. https://doi.org/10.1002/widm.1318CrossRef

43.

Hofner B, Mayr A, Robinzonov N, Schmid M (2014) Model-based boosting in R: a hands-on tutorial using the R package mboost. Comput Stat 29(1–2):3–35. https://doi.org/10.1007/s00180-012-0382-5MathSciNetCrossRefMATH

44.

Hothorn T, Bühlmann P (2006) Model-based boosting in high dimensions. Bioinformatics 22(22):2828–2829. https://doi.org/10.1093/bioinformatics/btl462CrossRef

45.

Hothorn T, Bühlmann P, Kneib T, Schmid M, Hofner B (2010) Model-based boosting 2.0. J Mach Learn Res 11:2109–2113MathSciNetMATH

46.

James G, Witten D, Hastie T, Tibshirani R (2013) An introduction to statistical learning. Springer, New York, NYCrossRef

47.

Jiang D, Ma T, Ding F, Fu J, Hao M, Wang Q, Chen S (2019) Mapping global environmental suitability for sorghum bicolor (L.) Moench. Energies 12(10):12101928. https://doi.org/10.3390/en12101928CrossRef

48.

Jiang D, Wang Q, Ding F, Fu J, Hao M (2019) Potential marginal land resources of cassava worldwide: a data-driven analysis. Renew Sustain Energy Rev 104:167–173. https://doi.org/10.1016/j.rser.2019.01.024CrossRef

49.

Jin M, Cao P, Short MP (2019) Predicting the onset of void swelling in irradiated metals with machine learning. J Nucl Mater 523:189–197. https://doi.org/10.1016/j.jnucmat.2019.05.054CrossRef

50.

Kabir E, Guikema SD, Quiring SM (2019) Predicting thunderstorm-induced power outages to support utility restoration. IEEE Trans Power Syst 34(6):4370–4381. https://doi.org/10.1109/TPWRS.2019.2914214CrossRef

51.

Ke G, Meng Q, Finkey T, Wang T, Chen W, Ma W, Ye Q, Liu TY (2017) LightGBM: a highly efficient gradient boosting decision tree. Adv Neural Inf Process Syst 30:3146–3154

52.

Kusiak A, Zhang Z (2010) Short-horizon prediction of wind power: a data-driven approach. IEEE Trans Energy Convers 25(4):1112–1122. https://doi.org/10.1109/TEC.2010.2043436CrossRef

53.

Kusiak A, Song Z, Zheng H (2009) Anticipatory control of wind turbines with data-driven predictive models. IEEE Trans Energy Convers 24(3):766–774. https://doi.org/10.1109/TEC.2009.2025320CrossRef

54.

Kusiak A, Zheng H, Song Z (2009) On-line monitoring of power curves. Renew Energy 34(6):1487–1493. https://doi.org/10.1016/j.renene.2008.10.022CrossRef

55.

Kusiak A, Zheng H, Song Z (2009) Short-term prediction of wind farm power: a data mining approach. IEEE Trans Energy Convers 24(1):125–136. https://doi.org/10.1109/TEC.2008.2006552CrossRef

56.

Kusiak A, Zheng H, Song Z (2009) Wind farm power prediction: a data-mining approach. Wind Energy 12(3):275–293. https://doi.org/10.1002/we.295CrossRef

57.

Lago J, De Brabandere K, De Ridder F, De Schutter B (2018) Short-term forecasting of solar irradiance without local telemetry: a generalized model using satellite data. Sol Energy 173:566–577. https://doi.org/10.1016/j.solener.2018.07.050CrossRef

58.

Li P, Zhang J-S (2018) A new hybrid method for China’s energy supply security forecasting based on ARIMA and XGBoost. Energies 11(7):1687. https://doi.org/10.3390/en11071687CrossRef

59.

Li Z, Hurn AS, Clements AE (2017) Forecasting quantiles of day-ahead electricity load. Energy Econ 67:60–71. https://doi.org/10.1016/j.eneco.2017.08.002CrossRef

60.

Liu H, Tian H-Q, Li Y-F, Zhang L (2015) Comparison of four Adaboost algorithm based artificial neural networks in wind speed predictions. Energy Convers Manage 92:67–81. https://doi.org/10.1016/j.enconman.2014.12.053CrossRef

61.

Liu H, Chen C, Lv X, Wu X, Liu M (2019) Deterministic wind energy forecasting: a review of intelligent predictors and auxiliary methods. Energy Convers Manage 195:328–345. https://doi.org/10.1016/j.enconman.2019.05.020CrossRef

62.

Ma J, Cheng JCP (2017) Identification of the numerical patterns behind the leading counties in the U.S. local green building markets using data mining. J Clean Prod 151:406–418. https://doi.org/10.1016/j.jclepro.2017.03.083CrossRef

63.

Mayr A, Hofner B (2018) Boosting for statistical modelling: a non-technical introduction. Stat Model 18(3–4):365–384. https://doi.org/10.1177/1471082X17748086MathSciNetCrossRefMATH

64.

Mayr A, Binder H, Gefeller O, Schmid M (2014) Extending statistical boosting. Methods Inf Med 53(06):428–435. https://doi.org/10.3414/ME13-01-0123CrossRef

65.

Mayr A, Binder H, Gefeller O, Schmid M (2014) The evolution of boosting algorithms. Methods Inf Med 53(06):419–427. https://doi.org/10.3414/ME13-01-0122CrossRef

66.

Mayr A, Hofner B, Waldmann E, Hepp T, Meyer S, Gefeller O (2017) An update on statistical boosting in biomedicine. Comput Math Methods Med. https://doi.org/10.1155/2017/6083072CrossRefMATH

67.

Mease D, Wyner A (2008) Evidence contrary to the statistical view of boosting. J Mach Learn Res 9:131–156

68.

Natekin A, Knoll A (2013) Gradient boosting machines, a tutorial. Front Neurorobot 7:21. https://doi.org/10.3389/fnbot.2013.00021CrossRef

69.

Obringer R, Kumar R, Nateghi R (2019) Analyzing the climate sensitivity of the coupled water-electricity demand nexus in the Midwestern United States. Appl Energy 252:113466. https://doi.org/10.1016/j.apenergy.2019.113466CrossRef

70.

Okwu MO, Nwachukwu AN (2019) A review of fuzzy logic applications in petroleum exploration, production and distribution operations. J Pet Explor Prod Technol 9(2):1555–1568. https://doi.org/10.1007/s13202-018-0560-2CrossRef

71.

Olofsson KEJ, Humphreys DA, Haye RJL (2018) Event hazard function learning and survival analysis for tearing mode onset characterization. Plasma Phys Controlled Fusion 60(8):084002. https://doi.org/10.1088/1361-6587/aac662CrossRef

72.

Olofsson KEJ, Sammuli BS, Humphreys DA (2019) Hazard function exploration of tokamak tearing mode stability boundaries. Fusion Eng Des 146:1476–1479. https://doi.org/10.1016/j.fusengdes.2019.02.109CrossRef

73.

Papacharalampous G, Tyralis H, Koutsoyiannis D (2019) Comparison of stochastic and machine learning methods for multi-step ahead forecasting of hydrological processes. Stoch Env Res Risk Assess 33(2):481–514. https://doi.org/10.1007/s00477-018-1638-6CrossRef

74.

Papacharalampous G, Tyralis H, Langousis A, Jayawardena AW, Sivakumar B, Mamassis N, Montanari A, Koutsoyiannis D (2019) Probabilistic hydrological post-processing at scale: why and how to apply machine-learning quantile regression algorithms. Water 11(10):2126. https://doi.org/10.3390/w11102126CrossRef

75.

Prokhorenkova L, Gusev G, Vorobev A, Dorogush AV, Gulin A (2018) CatBoost: unbiased boosting with categorical features. Adv Neural Inf Process Syst 31:6638–6648

76.

Puig BC, Carmona J (2019) Bridging the gap between energy consumption and distribution through non-technical loss detection. Energies 12(9):1748. https://doi.org/10.3390/en12091748CrossRef

77.

Razavi-Far R, Baraldi P, Zio E (2012) Dynamic weighting ensembles for incremental learning and diagnosing new concept class faults in nuclear power systems. IEEE Trans Nucl Sci 59(5):2520–2530. https://doi.org/10.1109/TNS.2012.2209125CrossRef

78.

Ridgeway G (1999) The state of boosting. Comput Sci Stat 31:172–181

79.

Robinson C, Dilkina B, Hubbs J, Zhang W, Guhathakurta S, Brown MA, Pendyala RM (2017) Machine learning approaches for estimating commercial building energy consumption. Appl Energy 208:889–904. https://doi.org/10.1016/j.apenergy.2017.09.060CrossRef

80.

Runge J, Zmeureanu R (2019) Forecasting energy use in buildings using artificial neural networks: a review. Energies 12(17):3254. https://doi.org/10.3390/en12173254CrossRef

81.

Sagi O, Rokach L (2018) Ensemble learning: a survey. Wiley Interdiscip Rev Data Min Knowl Discov 8(4):e1249. https://doi.org/10.1002/widm.1249CrossRef

82.

Salcedo-Sanz S, Cornejo-Bueno L, Prieto L, Paredes D, García-Herrera R (2018) Feature selection in machine learning prediction systems for renewable energy applications. Renew Sustain Energy Rev 90:728–741. https://doi.org/10.1016/j.rser.2018.04.008CrossRef

83.

Schapire RE (1990) The strength of weak learnability. Mach Learn 5(2):197–227. https://doi.org/10.1007/BF00116037CrossRef

84.

Schapire RE, Freund Y (2012) Boosting. The MIT Press, Cambridge, MassachusettsMATH

85.

Schapire RE, Freund Y, Bartlett P, Lee WS (1998) Boosting the margin: a new explanation for the effectiveness of voting methods. Ann Stat 26(5):1651–1686. https://doi.org/10.1214/aos/1024691352MathSciNetCrossRefMATH

86.

Sha H, Xu P, Yang Z, Chen Y, Tang J (2019) Overview of computational intelligence for building energy system design. Renew Sustain Energy Rev 108:76–90. https://doi.org/10.1016/j.rser.2019.03.018CrossRef

87.

Shafik N, Tutz G (2009) Boosting nonlinear additive autoregressive time series. Comput Stat Data Anal 53(7):2453–2464. https://doi.org/10.1016/j.csda.2008.12.006MathSciNetCrossRefMATH

88.

Sharma A, Kakkar A (2018) Forecasting daily global solar irradiance generation using machine learning. Renew Sustain Energy Rev 82(Part 3):2254–2269. https://doi.org/10.1016/j.rser.2017.08.066CrossRef

89.

Sobri S, Koohi-Kamali S, Rahim NA (2018) Solar photovoltaic generation forecasting methods: a review. Energy Convers Manage 156:459–497. https://doi.org/10.1016/j.enconman.2017.11.019CrossRef

90.

Souza FAA, Neto TRF, Magalhaes FRP, Silva FB, Pontes RST (2017) Predicting the grounding topology based on grounding impedance & the pattern recognition framework: a case study on one to four ground rods in straight line. IEEE Trans Power Deliv 32(4):1748–1757. https://doi.org/10.1109/TPWRD.2016.2626339CrossRef

91.

Stetco A, Dinmohammadi F, Zhao X, Robu V, Flynn D, Barnes M, Keane J, Nenadic G (2019) Machine learning methods for wind turbine condition monitoring: a review. Renew Energy 133:620–635. https://doi.org/10.1016/j.renene.2018.10.047CrossRef

92.

Sugiawan Y, Kurniawan R, Managi S (2019) Are carbon dioxide emission reductions compatible with sustainable well-being? Appl Energy 242:1–11. https://doi.org/10.1016/j.apenergy.2019.03.113CrossRef

93.

Sun Y, Gao C, Li J, Wang R, Liu J (2019) Evaluating urban heat island intensity and its associated determinants of towns and cities continuum in the Yangtze River Delta urban agglomerations. Sustain Urban Areas 50:101659. https://doi.org/10.1016/j.scs.2019.101659CrossRef

94.

Suryanarayana G, Lago J, Geysen D, Aleksiejuk P, Johansson C (2018) Thermal load forecasting in district heating networks using deep learning and advanced feature selection methods. Energy 157:141–149. https://doi.org/10.1016/j.energy.2018.05.111CrossRef

95.

Toyao T, Suzuki K, Kikuchi S, Takakusagi S, Shimizu K-I, Takigawa I (2018) Toward effective utilization of methane: machine learning prediction of adsorption energies on metal alloys. J Phys Chem C 122(15):8315–8326. https://doi.org/10.1021/acs.jpcc.7b12670CrossRef

96.

Tutz G, Binder H (2006) Generalized additive modelling with implicit variable selection by likelihood-based boosting. Biometrics 62(4):961–971. https://doi.org/10.1111/j.1541-0420.2006.00578.xMathSciNetCrossRefMATH

97.

Tyralis H, Papacharalampous G, Burnetas A, Langousis A (2019) Hydrological post-processing using stacked generalization of quantile regression algorithms: large-scale application over CONUS. J Hydrol 577:123957. https://doi.org/10.1016/j.jhydrol.2019.123957CrossRef

98.

Tyralis H, Papacharalampous G, Langousis A (2019) A brief review of random forests for water scientists and practitioners and their recent history in water resources. Water 11(5):910. https://doi.org/10.3390/w11050910CrossRef

99.

Urraca R, Martinez-de-Pison E, Sanz-Garcia A, Antonanzas J, Antonanzas-Torres F (2017) Estimation methods for global solar radiation: case study evaluation of five different approaches in central Spain. Renew Sustain Energy Rev 77:1098–1113. https://doi.org/10.1016/j.rser.2016.11.222CrossRef

100.

Vázquez-Canteli JR, Nagy Z (2019) Reinforcement learning for demand response: a review of algorithms and modeling techniques. Appl Energy 235:1072–1089. https://doi.org/10.1016/j.apenergy.2018.11.002CrossRef

101.

Voyant C, Notton G, Kalogirou S, Nivet ML, Paoli C, Motte F, Fouilloy A (2017) Machine learning methods for solar radiation forecasting: a review. Renew Energy 105:569–582. https://doi.org/10.1016/j.renene.2016.12.095CrossRef

102.

Wang H, Lei Z, Zhang X, Zhou B, Peng J (2019) A review of deep learning for renewable energy forecasting. Energy Convers Manage 198:111799. https://doi.org/10.1016/j.enconman.2019.111799CrossRef

103.

Wu T, Wang J (2019) Global discovery of stable and non-toxic hybrid organic-inorganic perovskites for photovoltaic systems by combining machine learning method with first principle calculations. Nano Energy 66:104070. https://doi.org/10.1016/j.nanoen.2019.104070CrossRef

104.

Wu X, Xiang S, Su J, Cai W (2019) Understanding quantitative relationship between methane storage capacities and characteristic properties of metal-organic frameworks based on machine learning. J Phys Chem C 123(14):8550–8559. https://doi.org/10.1021/acs.jpcc.8b11793CrossRef

105.

Wyner AJ, Olson M, Bleich J, Mease D (2017) Explaining the success of AdaBoost and random forests as interpolating classifiers. J Mach Learn Res 18(48):1–33MathSciNetMATH

106.

Yagli GM, Yang D, Srinivasan D (2019) Automatic hourly solar forecasting using machine learning models. Renew Sustain Energy Rev 105:487–498. https://doi.org/10.1016/j.rser.2019.02.006CrossRef

107.

Yoshihashi R, Kawakami R, Iida M, Naemura T (2017) Bird detection and species classification with time-lapse images around a wind farm: dataset construction and evaluation. Wind Energy 20(12):1983–1995. https://doi.org/10.1002/we.2135CrossRef

108.

Yuan T, Sun Z, Ma S (2019) Gearbox fault prediction of wind turbines based on a stacking model and change-point detection. Energies 12(22):4224. https://doi.org/10.3390/en12224224CrossRef

109.

Zamo M, Mestre O, Arbogast P, Pannekoucke O (2014) A benchmark of statistical regression methods for short-term forecasting of photovoltaic electricity production, part I: deterministic forecast of hourly production. Sol Energy 105:792–803. https://doi.org/10.1016/j.solener.2013.12.006CrossRef

110.

Zhang Z, Kusiak A (2011) Models for optimization of energy consumption of pumps in a wastewater processing plant. J Energy Eng 137(4):159–168. https://doi.org/10.1061/(ASCE)EY.1943-7897.0000051CrossRef

111.

Zhang W, Quan H, Srinivasan D (2018) Parallel and reliable probabilistic load forecasting via quantile regression forest and quantile determination. Energy 160:810–819. https://doi.org/10.1016/j.energy.2018.07.019CrossRef

112.

Zhao Y, Li T, Zhang X, Zhang C (2019) Artificial intelligence-based fault detection and diagnosis methods for building energy systems: advantages, challenges and the future. Renew Sustain Energy Rev 109:85–101. https://doi.org/10.1016/j.rser.2019.04.021CrossRef

113.

Zheng H, Kusiak A (2009) Prediction of wind farm power ramp rates: a data-mining approach. J Sol Energy Eng 131(3):0310111. https://doi.org/10.1115/1.3142727CrossRef

114.

Zhou ZH (2012) Ensemble methods: foundations and algorithms. Chapman and Hall/CRC, Boca Raton, FLCrossRef

115.

Zhu R, Guo W, Gong X (2019) Short-term photovoltaic power output prediction based on k-fold cross-validation and an ensemble model. Energies 12(7):1220. https://doi.org/10.3390/en12071220CrossRef

Titel: Boosting algorithms in energy research: a systematic review
verfasst von: Hristos Tyralis
Georgia Papacharalampous
Publikationsdatum: 19.04.2021
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 21/2021
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-021-05995-8

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