Precise forecasting of renewable energy generation is crucial for ensuring grid stability and enhancing the efficiency of energy management systems. This research develops and rigorously evaluates a range of deep learning models—such as Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, Gated Recurrent Units (GRUs), and Bidirectional LSTM (BiLSTM) architectures—for predicting solar, wind, and total renewable energy production at a national scale. These models are systematically benchmarked against traditional machine learning approaches and gradient boosting methods to determine their predictive capabilities. The findings demonstrate that deep learning models incorporating memory mechanisms consistently surpass conventional methods, with BiLSTM standing out as the most precise and dependable model. Furthermore, the study investigates fully connected artificial neural networks (ANNs) and ConvLSTM2D models, reinforcing the advantages of memory-based architectures in modeling temporal relationships. By introducing a robust deep learning framework for large-scale renewable energy forecasting, this research represents a considerable leap forward compared to traditional machine learning techniques. The results highlight the transformative potential of deep learning in improving forecasting accuracy, thereby facilitating more effective energy planning and the smooth integration of renewable energy into national power grids.
WangHLeiZZhangX, et al.A review of deep learning for renewable energy forecasting. Energy Convers Manage2019; 198: 111799.
2.
SrinivasanSKumarasamySAndreadakisZE, et al.Artificial intelligence and mathematical models of power grids driven by renewable energy sources: a survey. Energies2023; 16: 5383.
3.
KimHLeeD. Probabilistic solar power forecasting based on bivariate conditional solar irradiation distributions. IEEE Trans Sustain Energy2021; 12: 2031–2041.
4.
Al-DahidiSAyadiOAlrbaiM, et al.Ensemble approach of optimized artificial neural networks for solar photovoltaic power prediction. IEEE Access2019; 7: 81741–81758.
5.
WenSZhangCLanH, et al.A hybrid ensemble model for interval prediction of solar power output in ship onboard power systems. IEEE Trans Sustain Energy2021; 12: 14–24.
6.
MishraMByomakesha DashPNayakJ, et al.Deep learning and wavelet transform integrated approach for short-term solar PV power prediction. Measurement2020; 166: 108250.
7.
GaoYMiyataSAkashiY. Interpretable deep learning models for hourly solar radiation prediction based on graph neural network and attention. Appl Energy2022; 321: 119288.
8.
YueHAliMMLinY, et al.Ultra-short-term forecasting of large distributed solar PV fleets using sparse smart inverter data. IEEE Trans Sustain Energy2024; 15: 1968–1980.
9.
ChaiMXiaFHaoS, et al.PV power prediction based on LSTM with adaptive hyperparameter adjustment. IEEE Access2019; 7: 115473.
10.
TangNMaoSWangY, et al.Solar power generation forecasting with a LASSO-based approach. IEEE Internet Things J2018; 5: 1090–1099.
11.
ZhangYBeaudinMTaheriR, et al.Day-ahead power output forecasting for small-scale solar photovoltaic electricity generators. IEEE Trans Smart Grid2015; 6: 2253–2262.
12.
ChengLZangHWeiZ, et al.Short-term solar power prediction learning directly from satellite images with regions of interest. IEEE Trans Sustain Energy2022; 13: 629–639.
13.
GhimireSNguyen-HuyTDeoRC, et al.Efficient daily solar radiation prediction with deep learning 4-phase convolutional neural network, dual stage stacked regression and support vector machine CNN-REGST hybrid model. Sustain Mater Technol2022; 32: e00429.
14.
AksoyNGencI. Improving accuracy in solar power plant power generation prediction: a hybrid model proposal. Turk J Elec Power Energy Syst2025; 5: 10–18.
15.
BaeKYJangHSSungDK. Hourly solar irradiance prediction based on support vector machine and its error analysis. IEEE Trans Power Syst2017; 32: 935–945.
16.
Singla PardeepDMSahoraS. An ensemble method to forecast 24-h ahead solar irradiance using wavelet decomposition and BiLSTM deep learning network. Earth Sci Inform2022; 15: 291–306.
17.
AksoyNGencI. Predictive models development using gradient boosting based methods for solar power plants. J Comput Sci2023; 67: 101958.
18.
MaZChangHSunZ, et al.Very short-term renewable energy power prediction using XGBoost optimized by TPE algorithm. In: 2020 4th International conference on HVDC (HVDC), 2020, pp.1236–1241. DOI: 10.1109/HVDC50696.2020.9292870.
19.
JuYSunGChenQ, et al.A model combining convolutional neural network and LightGBM algorithm for ultra-short-term wind power forecasting. IEEE Access2019; 7: 28309–28318.
20.
RenJYuZGaoG, et al.A CNN-LSTM-LightGBM based short-term wind power prediction method based on attention mechanism. Energy Rep2022; 8: 437–443. ICPE 2021-The 2nd International Conference on Power Engineering.
21.
LuSBaoT. Short-term electricity load forecasting based on NeuralProphet and CNN-LSTM. IEEE Access2024; 12: 76870–76879.
22.
ChengH-YYuC-CLinC-L. Day-ahead to week-ahead solar irradiance prediction using convolutional long short-term memory networks. Renew Energy2021; 179: 2300–2308.
23.
ZhangQYanHLiuY. Power generation forecasting for solar plants based on dynamic Bayesian networks by fusing multi-source information. Renew Sustain Energy Rev2024; 202: 114691.
24.
RangelovDBoergerMTcholtchevN, et al.Design and development of a short-term photovoltaic power output forecasting method based on random forest, deep neural network and LSTM using readily available weather features. IEEE Access2023; 11: 41578–41595.
25.
TajjourSChandelSSAlotaibiMA, et al.Short-term solar irradiance forecasting using deep learning techniques: A comprehensive case study. IEEE Access2023; 11: 119851.
26.
NelegaRGreuDIJecanE, et al.Prediction of power generation of a photovoltaic power plant based on neural networks. IEEE Access2023; 11: 20713–20724.
27.
SharkawyA-NAliMMMousaHHH, et al.Solar PV power estimation and upscaling forecast using different artificial neural networks types: Assessment, validation, and comparison. IEEE Access2023; 11: 19279–19300.
28.
GaboitaolelweJZungeruAMYahyaA, et al.Machine learning based solar photovoltaic power forecasting: A review and comparison. IEEE Access2023; 11: 40820–40845.
29.
AksoyNGencI. Energy storage management for microgrids using n-step bootstrapping. IEEE Can J Elect Comput Eng2023; 46: 107–116.
30.
ForuzanESohL-KAsgarpoorS. Reinforcement learning approach for optimal distributed energy management in a microgrid. IEEE Trans Power Syst2018; 33: 5749–5758.
31.
AbisoyeBOSunYZenghuiW, et al.Methodologies and insights. Renew Energy Focus2024; 48: 100529.
32.
SunLLiuTXieY, et al.Real-time power prediction approach for turbine using deep learning techniques. Energy2021; 233: 121130.
33.
AlmutairiAAlrumayhO. An intelligent deep learning based prediction model for wind power generation. Comput Electr Eng2022; 101: 108000.
34.
DeepaSNBanerjeeA. Intelligent neural learning models for multi-step wind speed forecasting in renewable energy applications. J Control Autom Electr Syst2022; 33: 881–900.
35.
LiaoWBak-JensenBPillaiJR, et al.Short-term power prediction for renewable energy using hybrid graph convolutional network and long short-term memory approach. Electr Power Syst Res2022; 211: 108614.
36.
ElmanJL. Finding structure in time. Cogn Sci1990; 14: 179–211.
37.
JordanMIRumelhartDE. Forward models: supervised learning with a distal teacher. Cogn Sci1992; 16: 307–354.
38.
HochreiterSSchmidhuberJ. Long short-term memory. Neural Comput1997; 9: 1735–1780.
39.
YuYSiXHuC, et al.A review of recurrent neural networks: LSTM cells and network architectures. Neural Comput2019; 31: 1235–1270.
40.
ChoKvan MerrienboerBBahdanauD, et al.On the properties of neural machine translation: encoder-decoder approaches. CoRR. abs/1409.1259, 2014.
41.
SchusterMPaliwalKK. Bidirectional recurrent neural networks. IEEE Trans Signal Process1997; 45: 2673–2681.
