Industrial and Manufacturing Engineering; Strategy and Management; General Environmental Science; Renewable Energy, Sustainability and the Environment; Building and Construction
Disciplines :
Energy
Author, co-author :
Mubarak, Hamza
Hammoudeh, Ahmad Tayseer Ahmad ; Université de Mons - UMONS > Faculté Polytechnique > Service Information, Signal et Intelligence artificielle
Ahmad, Shameem
Abdellatif, Abdallah
Mekhilef, Saad
Mokhlis, Hazlie
Dupont, Stéphane ; Université de Mons - UMONS > Faculté des Science > Service d'Intelligence Artificielle
Language :
English
Title :
A hybrid machine learning method with explicit time encoding for improved Malaysian photovoltaic power prediction
Abdel-Nasser, M., Mahmoud, K., Accurate photovoltaic power forecasting models using deep LSTM-RNN. Neural Comput. Appl. 31:7 (2019), 2727–2740.
Ağbulut, Ü., Gürel, A.E., Ergün, A., Ceylan, İ., Performance assessment of a V-Trough photovoltaic system and prediction of power output with different machine learning algorithms. J. Clean. Prod., 268, 2020, 122269.
Ahmed, T., Mekhilef, S., Shah, R., Mithulananthan, N., An assessment of the solar photovoltaic generation yield in Malaysia using satellite derived datasets. Int. Energy J., 19(2), 2019.
Akhter, M.N., Mekhilef, S., Mokhlis, H., Ali, R., Usama, M., Muhammad, M.A., Khairuddin, A.S.M., A hybrid deep learning method for an hour ahead power output forecasting of three different photovoltaic systems. Appl. Energy, 2021, 118185.
Akhter, M.N., Mekhilef, S., Mokhlis, H., Ali, R., Usama, M., Muhammad, M.A., Khairuddin, A.S.M., A hybrid deep learning method for an hour ahead power output forecasting of three different photovoltaic systems. Appl. Energy, 307, 2022, 118185.
Akhter, M.N., Mekhilef, S., Mokhlis, H., Almohaimeed, Z.M., Muhammad, M.A., Khairuddin, A.S.M., Akram, R., Hussain, M.M., An hour-ahead PV power forecasting method based on an RNN-LSTM model for three different PV plants. Energies, 15(6), 2022, 2243.
Al-Hajj, R., Assi, A., Fouad, M., Short-term prediction of global solar radiation energy using weather data and machine learning ensembles: a comparative study. J. Sol. Energy Eng., 143(5), 2021, 051003.
Alzahrani, A., Shamsi, P., Dagli, C., Ferdowsi, M., Solar irradiance forecasting using deep neural networks. Procedia Comput. Sci. 114 (2017), 304–313.
Anang, N., Azman, S.S.N., Muda, W., Dagang, A., Daud, M.Z., Performance analysis of a grid-connected rooftop solar PV system in Kuala Terengganu, Malaysia. Energy Build., 248, 2021, 111182.
Antonanzas, J., Osorio, N., Escobar, R., Urraca, R., Martinez-de-Pison, F.J., Antonanzas-Torres, F., Review of photovoltaic power forecasting. Sol. Energy 136 (2016), 78–111.
Bae, K.Y., Jang, H.S., Sung, D.K., Hourly solar irradiance prediction based on support vector machine and its error analysis. IEEE Trans. Power Syst. 32:2 (2016), 935–945.
Breiman, L., Random forests. Mach. Learn. 45:1 (2001), 5–32.
Dairi, A., Harrou, F., Sun, Y., Khadraoui, S., Short-term forecasting of photovoltaic solar power production using variational auto-encoder driven deep learning approach. Appl. Sci., 10(23), 2020, 8400.
Das, U.K., Tey, K.S., Seyedmahmoudian, M., Mekhilef, S., Idris, M.Y.I., Van Deventer, W., Horan, B., Stojcevski, A., Forecasting of photovoltaic power generation and model optimization: a review. Renew. Sustain. Energy Rev. 81 (2018), 912–928.
Demšar, J., Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7 (2006), 1–30.
Farhoodnea, M., Mohamed, A., Khatib, T., Elmenreich, W., Performance evaluation and characterization of a 3-kWp grid-connected photovoltaic system based on tropical field experimental results: new results and comparative study. Renew. Sustain. Energy Rev. 42 (2015), 1047–1054.
Fernández-Delgado, M., Sirsat, M.S., Cernadas, E., Alawadi, S., Barro, S., Febrero-Bande, M., An extensive experimental survey of regression methods. Neural Network. 111 (2019), 11–34.
Hammoudeh, A., Dupont, S., How does explicit orientation encoding affect image classification of ConvNets?. IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshop, 2022 NeuroVision.
Hossain, M., Mekhilef, S., Danesh, M., Olatomiwa, L., Shamshirband, S., Application of extreme learning machine for short term output power forecasting of three grid-connected PV systems. J. Clean. Prod. 167 (2017), 395–405.
Hussain, S., AlAlili, A., A hybrid solar radiation modeling approach using wavelet multiresolution analysis and artificial neural networks. Appl. Energy 208 (2017), 540–550.
Jang, H.S., Bae, K.Y., Park, H.-S., Sung, D.K., Solar power prediction based on satellite images and support vector machine. IEEE Trans. Sustain. Energy 7:3 (2016), 1255–1263.
Jiang, H., Dong, Y., Xiao, L., A multi-stage intelligent approach based on an ensemble of two-way interaction model for forecasting the global horizontal radiation of India. Energy Convers. Manag. 137 (2017), 142–154.
Khan, W., Walker, S., Zeiler, W., Improved solar photovoltaic energy generation forecast using deep learning-based ensemble stacking approach. Energy, 240, 2022, 122812.
Kumar, R., Goel, P., Exploring the domain of interpretive structural modelling (ISM) for sustainable future panorama: a bibliometric and content analysis. Arch. Comput. Methods Eng., 2021, 1–30.
Kumari, P., Toshniwal, D., Extreme gradient boosting and deep neural network based ensemble learning approach to forecast hourly solar irradiance. J. Clean. Prod., 279, 2021, 123285.
Lateko, A.A., Yang, H.-T., Huang, C.-M., Short-term PV power forecasting using a regression-based ensemble method. Energies, 15(11), 2022, 4171.
Li, K., Wang, F., Mi, Z., Fotuhi-Firuzabad, M., Duić, N., Wang, T., Capacity and output power estimation approach of individual behind-the-meter distributed photovoltaic system for demand response baseline estimation. Appl. Energy, 253, 2019, 113595.
Li, K., Yan, J., Hu, L., Wang, F., Zhang, N., Two-stage decoupled estimation approach of aggregated baseline load under high penetration of behind-the-meter PV system. IEEE Trans. Smart Grid 12:6 (2021), 4876–4885.
Lima, F.J., Martins, F.R., Pereira, E.B., Lorenz, E., Heinemann, D., Forecast for surface solar irradiance at the Brazilian Northeastern region using NWP model and artificial neural networks. Renew. Energy 87 (2016), 807–818.
Liu, D., Sun, K., Random forest solar power forecast based on classification optimization. Energy, 187, 2019, 115940.
Lu, G.-F., Zou, J., Wang, Y., L1-norm and maximum margin criterion based discriminant locality preserving projections via trace Lasso. Pattern Recogn. 55 (2016), 207–214.
Lundberg, S.M., Lee, S.-I., A unified approach to interpreting model predictions. Adv. Neural Inf. Process. Syst., 30, 2017.
Malaysia, S.E.D.A., SEDA Malaysia Grid-Connected PV System Course Design. 2016.
Mubarak, H., Mansor, N.N., Mokhlis, H., Mohamad, M., Mohamad, H., Muhammad, M.A., Al Samman, M., Afzal, S., Optimum distribution system expansion planning incorporating DG based on N-1 criterion for sustainable system. Sustainability, 13(12), 2021, 6708.
Mubarak, H., Muhammad, M.A., Mansor, N.N., Mokhlis, H., Ahmad, S., Ahmed, T., Sufyan, M., Operational cost minimization of electrical distribution network during switching for sustainable operation. Sustainability, 14(7), 2022, 4196.
Narvaez, G., Giraldo, L.F., Bressan, M., Pantoja, A., Machine learning for site-adaptation and solar radiation forecasting. Renew. Energy 167 (2021), 333–342.
Niu, D., Wang, K., Sun, L., Wu, J., Xu, X., Short-term photovoltaic power generation forecasting based on random forest feature selection and CEEMD: a case study. Appl. Soft Comput., 93, 2020, 106389.
Olatomiwa, L., Mekhilef, S., Shamshirband, S., Petković, D., Adaptive neuro-fuzzy approach for solar radiation prediction in Nigeria. Renew. Sustain. Energy Rev. 51 (2015), 1784–1791.
Pedro, H.T., Coimbra, C.F., Short-term irradiance forecastability for various solar micro-climates. Sol. Energy 122 (2015), 587–602.
Qing, X., Niu, Y., Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM. Energy 148 (2018), 461–468.
Saadatian, O., Sopian, K., Elhab, B., Ruslan, M., Asim, N., Optimal solar panels' tilt angles and orientations in Kuala Lumpur, Malaysia. Adv. Environ., Biotechnol. Biomed., 2013.
Shi, H., Xu, M., Li, R., Deep learning for household load forecasting—a novel pooling deep RNN. IEEE Trans. Smart Grid 9:5 (2017), 5271–5280.
Shireen, T., Shao, C., Wang, H., Li, J., Zhang, X., Li, M., Iterative multi-task learning for time-series modeling of solar panel PV outputs. Appl. Energy 212 (2018), 654–662.
Srivastava, S., Lessmann, S., A comparative study of LSTM neural networks in forecasting day-ahead global horizontal irradiance with satellite data. Sol. Energy 162 (2018), 232–247.
Tang, N., Mao, S., Wang, Y., Nelms, R., Solar power generation forecasting with a LASSO-based approach. IEEE Internet Things J. 5:2 (2018), 1090–1099.
Tibshirani, R., Regression shrinkage and selection via the lasso. J. Roy. Stat. Soc. B 58:1 (1996), 267–288.
VanDeventer, W., Jamei, E., Thirunavukkarasu, G.S., Seyedmahmoudian, M., Soon, T.K., Horan, B., Mekhilef, S., Stojcevski, A., Short-term PV power forecasting using hybrid GASVM technique. Renew. Energy 140 (2019), 367–379.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I., Attention is all you need. Adv. Neural Inf. Process. Syst., 30, 2017.
Wang, F., Zhen, Z., Liu, C., Mi, Z., Hodge, B.-M., Shafie-khah, M., Catalão, J.P., Image phase shift invariance based cloud motion displacement vector calculation method for ultra-short-term solar PV power forecasting. Energy Convers. Manag. 157 (2018), 123–135.
Wang, L., Zheng, D., Cheng, G., An adaptability evaluation of large-scale solar energy for hot water application based on energy-economic-environment consideration: a case study of city-residential buildings in China. J. Clean. Prod., 296, 2021, 126585.
Wang, X., Sun, Y., Luo, D., Peng, J., Comparative study of machine learning approaches for predicting short-term photovoltaic power output based on weather type classification. Energy, 240, 2022, 122733.
Wen, L., Zhou, K., Yang, S., Lu, X., Optimal load dispatch of community microgrid with deep learning based solar power and load forecasting. Energy 171 (2019), 1053–1065.
Wolff, B., Kühnert, J., Lorenz, E., Kramer, O., Heinemann, D., Comparing support vector regression for PV power forecasting to a physical modeling approach using measurement, numerical weather prediction, and cloud motion data. Sol. Energy 135 (2016), 197–208.
Wu, J., Chan, C.K., Prediction of hourly solar radiation with multi-model framework. Energy Convers. Manag. 76 (2013), 347–355.
Yang, C., Thatte, A.A., Xie, L., Multitime-scale data-driven spatio-temporal forecast of photovoltaic generation. IEEE Trans. Sustain. Energy 6:1 (2014), 104–112.
Yang, H.-T., Huang, C.-M., Huang, Y.-C., Pai, Y.-S., A weather-based hybrid method for 1-day ahead hourly forecasting of PV power output. IEEE Trans. Sustain. Energy 5:3 (2014), 917–926.
Yu, J., Tang, Y.M., Chau, K.Y., Nazar, R., Ali, S., Iqbal, W., Role of solar-based renewable energy in mitigating CO2 emissions: evidence from quantile-on-quantile estimation. Renew. Energy 182 (2022), 216–226.
Zaher, A., Thil, S., Nou, J., Traoré, A., Grieu, S., Comparative study of algorithms for cloud motion estimation using sky-imaging data. IFAC-PapersOnLine 50:1 (2017), 5934–5939.
Zang, H., Cheng, L., Ding, T., Cheung, K.W., Liang, Z., Wei, Z., Sun, G., Hybrid method for short-term photovoltaic power forecasting based on deep convolutional neural network. IET Gener., Transm. Distrib. 12:20 (2018), 4557–4567.
Zhang, H., Zhu, T., Stacking model for photovoltaic-power-generation prediction. Sustainability, 14(9), 2022, 5669.
Zhang, J., Verschae, R., Nobuhara, S., Lalonde, J.-F., Deep photovoltaic nowcasting. Sol. Energy 176 (2018), 267–276.
Zhen, Z., Liu, J., Zhang, Z., Wang, F., Chai, H., Yu, Y., Lu, X., Wang, T., Lin, Y., Deep learning based surface irradiance mapping model for solar PV power forecasting using sky image. IEEE Trans. Ind. Appl. 56:4 (2020), 3385–3396.
Zjavka, L., PV power intra-day predictions using PDE models of polynomial networks based on operational calculus. IET Renew. Power Gener. 14:8 (2020), 1405–1412.
