Day-ahead photovoltaic power prediction based on a hybrid gradient descent and metaheuristic optimizer

European Commission, “Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Comittee of the Regions, REPowerEU Plan,” Brussels, Available at: https://eur-lex.europa.eu/. European Commission, “Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Comittee of the Regions, EU Solar Energy Strategy ”, Brussels, 2022, Available at: https://eur-lex.europa.eu/. Gandhi, 2020, Review of power system impacts at high PV penetration Part I: Factors limiting PV penetration, Sol Energy, 210, 181, 10.1016/j.solener.2020.06.097 Cordova, 2018, An Efficient Forecasting-Optimization Scheme for the Intraday Unit Commitment Process Under Significant Wind and Solar Power, IEEE Trans Sustainable Energy, 9, 1899, 10.1109/TSTE.2018.2818979 Sánchez de la Nieta, 2020, Participation of photovoltaic power producers in short-term electricity markets based on rescheduling and risk-hedging mapping, Appl Energy, 266, 114741, 10.1016/j.apenergy.2020.114741 Pierro, 2021, Machine learning-based PV power forecasting methods for electrical grid management and energy trading, 165 Ahmed, 2020, A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization, Renew Sustain Energy Rev, 124, 10.1016/j.rser.2020.109792 Yang, 2021, Classification and summarization of solar irradiance and power forecasting methods: A thorough review, CSEE J Power Energy Syst, 1 Mayer, 2022, Benefits of physical and machine learning hybridization for photovoltaic power forecasting, Renew Sustain Energy Rev, 168, 10.1016/j.rser.2022.112772 Gigoni, 2018, Day-ahead hourly forecasting of power generation from photovoltaic plants, IEEE Trans Sustainable Energy, 9, 831, 10.1109/TSTE.2017.2762435 Theocharides, 2021, Comparative analysis of machine learning models for day-ahead photovoltaic power production forecasting, Energies, 14, 1081, 10.3390/en14041081 Markovics, 2022, Comparison of machine learning methods for photovoltaic power forecasting based on numerical weather prediction, Renew Sustain Energy Rev, 161, 10.1016/j.rser.2022.112364 Das, 2018, Forecasting of photovoltaic power generation and model optimization: A review, Renew Sustain Energy Rev, 81, 912, 10.1016/j.rser.2017.08.017 Visser, 2022, Operational day-ahead solar power forecasting for aggregated PV systems with a varying spatial distribution, Renew Energy, 183, 267, 10.1016/j.renene.2021.10.102 Zhou, 2019, Short-term photovoltaic power forecasting based on long short term memory neural network and attention mechanism, IEEE Access, 7, 78063, 10.1109/ACCESS.2019.2923006 Zamanidou, 2023, Day-ahead scheduling of a Hybrid Renewable Energy System based on generation forecasting using deep learning approach, Energy Sci Eng, 11, 1688, 10.1002/ese3.1413 Mubarak, 2023, A hybrid machine learning method with explicit time encoding for improved Malaysian photovoltaic power prediction, J Clean Prod, 382, 10.1016/j.jclepro.2022.134979 Abdel-Basset, 2021, PV-Net: An innovative deep learning approach for efficient forecasting of short-term photovoltaic energy production, J Clean Prod, 303, 10.1016/j.jclepro.2021.127037 Emami Javanmard, 2022, A hybrid model with applying machine learning algorithms and optimization model to forecast greenhouse gas emissions with energy market data, Sustanable Cities Soc, 82 Liu, 2021, Short-term photovoltaic power prediction on modal reconstruction: A novel hybrid model approach, Sustainable Energy Technol Assess, 45 Yan, 2021, Frequency-domain decomposition and deep learning based solar PV power ultra-short-term forecasting model, IEEE Trans Ind Appl, 57, 3282, 10.1109/TIA.2021.3073652 Alshammari, 2023, Generation forecasting employing Deep Recurrent Neural Network with metaheruistic feature selection methodology for Renewable energy power plants, Sustainable Energy Technol Assess, 55 Zhang, 2022, Wavelet-based neural network with genetic algorithm optimization for generation prediction of PV plants, Energy Rep, 8, 10976, 10.1016/j.egyr.2022.08.176 VanDeventer, 2019, Short-term PV power forecasting using hybrid GASVM technique, Renew Energy, 140, 367, 10.1016/j.renene.2019.02.087 Eseye, 2018, Short-term photovoltaic solar power forecasting using a hybrid Wavelet-PSO-SVM model based on SCADA and Meteorological information, Renew Energy, 118, 357, 10.1016/j.renene.2017.11.011 Liang, 2023, FCDT-IWBOA-LSSVR: An innovative hybrid machine learning approach for efficient prediction of short-to-mid-term photovoltaic generation, J Clean Prod, 385, 10.1016/j.jclepro.2022.135716 T. Dozat, “Incorporating Nesterov Momentum into Adam,” presented at the International Conference on Learning Representations, Puerto Rico, 2016. D. P. Kingma and J. Ba, “Adam: A Method for Stochastic Optimization,” presented at the 3rd International Conference for Learning Representations, San Diego, May, 2015. Brownlee, 2017, Introduction to time series forecasting with python: how to prepare data and develop models to predict the future, Machine Learn Mastery Kothona, 2022, A novel hybrid ensemble LSTM-FFNN forecasting model for very short-term and short-term PV generation forecasting, IET Renew Power Gener, 16, 3, 10.1049/rpg2.12209 Husein, 2019, Day-ahead solar irradiance forecasting for microgrids using a long short-term memory recurrent neural network: a deep learning approach, Energies, 12, 1856, 10.3390/en12101856 Ewees, 2022, HBO-LSTM: Optimized long short term memory with heap-based optimizer for wind power forecasting, Energ Conver Manage, 268, 10.1016/j.enconman.2022.116022 Das, 2022, Prophesying the short-term dynamics of the crude oil future price by adopting the survival of the fittest principle of improved grey optimization and extreme learning machine, Mathematics, 10, 10.3390/math10071121 Zhang, 2022, Enhanced salp search algorithm for optimization extreme learning machine and application to dew point temperature prediction, Internat J Comput Intell Syst, 15, 10.1007/s44196-022-00160-y Das, 2022, Effective forecasting of stock market price by using extreme learning machine optimized by PSO-based group oriented crow search algorithm, Neural Comput Applic, 34, 555, 10.1007/s00521-021-06403-x Hu, 2019, The improved antlion optimizer and artificial neural network for Chinese influenza prediction, Complexity, 2019, 1 Tripathi, 2023, Metaheuristic enabled intelligent model for stock market prediction via integrating volatility spillover: India and its Asian and European counterparts, Data Knowl Eng, 144, 10.1016/j.datak.2022.102127 Gu, 2023, Short-term wind power forecasting and uncertainty analysis based on FCM–WOA–ELM–GMM, Energy Rep, 9, 807, 10.1016/j.egyr.2022.11.202 Hou, 2021, Research on optimization of GWO-BP model for cloud server load prediction, IEEE Access, 9, 162581, 10.1109/ACCESS.2021.3132052 Parsopoulos, 2010 Duchi, 2011, Adaptive subgradient methods for online learning and stochastic optimization, J Machine Learn Res, 12, 2121 M. D. Zeiler, “ADADELTA: An Adaptive Learning Rate Method,” arxiv, 1212.5701, 2012, pp. 1-6. Mahmud, 2021, Machine learning based PV power generation forecasting in alice springs, IEEE Access, 9, 46117, 10.1109/ACCESS.2021.3066494 Panapakidis, 2020, Deep learning approach based on LSTM model for short-term solar generation forecasting Mellit, 2021, Deep learning neural networks for short-term photovoltaic power forecasting, Renew Energy, 172, 276, 10.1016/j.renene.2021.02.166 Luo, 2022, An adaptive deep learning framework for day-ahead forecasting of photovoltaic power generation, Sustainable Energy Technol Assess, 52 Huang, 2019, Forecasting hourly solar irradiance using hybrid wavelet transformation and Elman model in smart grid, IEEE Access, 7, 139909, 10.1109/ACCESS.2019.2943886 Liu, 2019, Wind speed forecasting models based on data decomposition, feature selection and group method of data handling network, Measurement, 148, 10.1016/j.measurement.2019.106971 Pelland, 2013, Photovoltaic and solar forecasting: state of the art Brownlee, 2016, Long short-term memory networks with python - develop deep learning models in Theano and TensorFlow Ising Keras, Machine Learn Mastery Rana, 2020, Multiple steps ahead solar photovoltaic power forecasting based on univariate machine learning models and data re-sampling, Sustain Energy Grids Networks, 21, 10.1016/j.segan.2019.100286 Zhang, 2015, A suite of metrics for assessing the performance of solar power forecasting, Sol Energy, 111, 157, 10.1016/j.solener.2014.10.016 Malvoni, 2016, Data on photovoltaic power forecasting models for Mediterranean climate, Data Brief, 7, 1639, 10.1016/j.dib.2016.04.063 World Weather Online. Available: www.worldweatheronline.com. [Accessed: 06/11/2022]. Visual Crossing. Available: https://www.visualcrossing.com/. [Accessed: 06/11/2022].