{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T09:40:06Z","timestamp":1784626806177,"version":"3.55.0"},"reference-count":60,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,1,13]],"date-time":"2023-01-13T00:00:00Z","timestamp":1673568000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The integration of solar energy with a power system brings great economic and environmental benefits. However, the high penetration of solar power is challenging due to the operation and planning of the existing power system owing to the intermittence and randomicity of solar power generation. Achieving accurate predictions for power generation is important to provide high-quality electric energy for end-users. Therefore, in this paper, we introduce a deep learning-based dual-stream convolutional neural network (CNN) and long short-term nemory (LSTM) network followed by a self-attention mechanism network (DSCLANet). Here, CNN is used to learn spatial patterns and LSTM is incorporated for temporal feature extraction. The output spatial and temporal feature vectors are then fused, followed by a self-attention mechanism to select optimal features for further processing. Finally, fully connected layers are incorporated for short-term solar power prediction. The performance of DSCLANet is evaluated on DKASC Alice Spring solar datasets, and it reduces the error rate up to 0.0136 MSE, 0.0304 MAE, and 0.0458 RMSE compared to recent state-of-the-art methods.<\/jats:p>","DOI":"10.3390\/s23020945","type":"journal-article","created":{"date-parts":[[2023,1,16]],"date-time":"2023-01-16T05:30:07Z","timestamp":1673847007000},"page":"945","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":87,"title":["Solar Power Prediction Using Dual Stream CNN-LSTM Architecture"],"prefix":"10.3390","volume":"23","author":[{"given":"Hamad","family":"Alharkan","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, Unaizah College of Engineering, Qassim University, Unaizah 56452, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6543-2520","authenticated-orcid":false,"given":"Shabana","family":"Habib","sequence":"additional","affiliation":[{"name":"Department of Information Technology, College of Computer, Qassim University, Buraydah 51452, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2379-4451","authenticated-orcid":false,"given":"Muhammad","family":"Islam","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, College of Engineering and Information Technology, Onaizah Colleges, Onaizah 56447, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"109725","DOI":"10.1016\/j.rser.2020.109725","article-title":"A deep learning-based forecasting model for renewable energy scenarios to guide sustainable energy policy: A case study of Korea","volume":"122","author":"Nam","year":"2020","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1016\/j.enpol.2016.12.050","article-title":"The unstudied barriers to widespread renewable energy deployment: Fossil fuel price responses","volume":"103","author":"Foster","year":"2017","journal-title":"Energy Policy"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"111799","DOI":"10.1016\/j.enconman.2019.111799","article-title":"A review of deep learning for renewable energy forecasting","volume":"198","author":"Wang","year":"2019","journal-title":"Energy Convers. Manag."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Aladhadh, S., Almatroodi, S.A., Habib, S., Alabdulatif, A., Khattak, S.U., and Islam, M. (2023). An Efficient Lightweight Hybrid Model with Attention Mechanism for Enhancer Sequence Recognition. Biomolecules, 13.","DOI":"10.3390\/biom13010070"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Alsharekh, M.F., Habib, S., Dewi, D.A., Albattah, W., Islam, M., and Albahli, S. (2022). Improving the Efficiency of Multistep Short-Term Electricity Load Forecasting via R-CNN with ML-LSTM. Sensors, 22.","DOI":"10.3390\/s22186913"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Yar, H., Imran, A.S., Khan, Z.A., Sajjad, M., and Kastrati, Z. (2021). Towards smart home automation using IoT-enabled edge-computing paradigm. Sensors, 21.","DOI":"10.3390\/s21144932"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"10230","DOI":"10.1016\/j.egyr.2022.08.009","article-title":"Towards efficient and effective renewable energy prediction via deep learning","volume":"8","author":"Khan","year":"2022","journal-title":"Energy Rep."},{"key":"ref_8","first-page":"102337","article-title":"Efficient short-term electricity load forecasting for effective energy management","volume":"53","author":"Khan","year":"2022","journal-title":"Sustain. Energy Technol. Assess."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Khan, F.A., Shees, M.M., Alsharekh, M.F., Alyahya, S., Saleem, F., Baghel, V., Sarwar, A., Islam, M., and Khan, S. (2021). Open-Circuit Fault Detection in a Multilevel Inverter Using Sub-Band Wavelet Energy. Electronics, 11.","DOI":"10.3390\/electronics11010123"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1016\/j.enconman.2017.03.056","article-title":"Assessing energy forecasting inaccuracy by simultaneously considering temporal and absolute errors","volume":"142","author":"Mallor","year":"2017","journal-title":"Energy Convers. Manag."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"3590","DOI":"10.1002\/er.6093","article-title":"Smart and intelligent energy monitoring systems: A comprehensive literature survey and future research guidelines","volume":"45","author":"Hussain","year":"2021","journal-title":"Int. J. Energy Res."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Habib, S., Alyahya, S., Islam, M., Alnajim, A.M., Alabdulatif, A., and Alabdulatif, A. (2023). Design and Implementation: An IoT-Framework-Based Automated Wastewater Irrigation System. Electronics, 12.","DOI":"10.3390\/electronics12010028"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Zuhaib, M., Shaikh, F.A., Tanweer, W., Alnajim, A.M., Alyahya, S., Khan, S., Usman, M., Islam, M., and Hasan, M.K. (2022). Faults Feature Extraction Using Discrete Wavelet Transform and Artificial Neural Network for Induction Motor Availability Monitoring\u2014Internet of Things Enabled Environment. Energies, 15.","DOI":"10.3390\/en15217888"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/j.solener.2019.02.044","article-title":"On post-processing day-ahead NWP forecasts using Kalman filtering","volume":"182","author":"Yang","year":"2019","journal-title":"Sol. Energy"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1016\/j.energy.2018.05.147","article-title":"Using a novel multi-variable grey model to forecast the electricity consumption of Shandong Province in China","volume":"157","author":"Wu","year":"2018","journal-title":"Energy"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Muhammad, T., Khan, A.U., Chughtai, M.T., Khan, R.A., Abid, Y., Islam, M., and Khan, S. (2022). An Adaptive Hybrid Control of Grid Tied Inverter for the Reduction of Total Harmonic Distortion and Improvement of Robustness against Grid Impedance Variation. Energies, 15.","DOI":"10.3390\/en15134724"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"556","DOI":"10.1016\/j.energy.2015.08.039","article-title":"A hybrid wind speed forecasting model based on phase space reconstruction theory and Markov model: A case study of wind farms in northwest China","volume":"91","author":"Wang","year":"2015","journal-title":"Energy"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/j.apenergy.2015.02.032","article-title":"Recursive wind speed forecasting based on Hammerstein Auto-Regressive model","volume":"145","author":"Maatallah","year":"2015","journal-title":"Appl. Energy"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Khan, K., Khan, R.U., Albattah, W., Nayab, D., Qamar, A.M., Habib, S., and Islam, M. (2021). Crowd Counting Using End-to-End Semantic Image Segmentation. Electronics, 10.","DOI":"10.3390\/electronics10111293"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1108","DOI":"10.1016\/j.rser.2016.12.015","article-title":"Building electrical energy consumption forecasting analysis using conventional and artificial intelligence methods: A review","volume":"70","author":"Daut","year":"2017","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1016\/j.enconman.2018.12.020","article-title":"A novel system based on neural networks with linear combination framework for wind speed forecasting","volume":"181","author":"Wang","year":"2019","journal-title":"Energy Convers. Manag."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"568","DOI":"10.1016\/j.apenergy.2016.01.130","article-title":"A wavelet-coupled support vector machine model for forecasting global incident solar radiation using limited meteorological dataset","volume":"168","author":"Deo","year":"2016","journal-title":"Appl. Energy"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1016\/j.renene.2017.12.023","article-title":"A new method based on Type-2 fuzzy neural network for accurate wind power forecasting under uncertain data","volume":"120","author":"Sharifian","year":"2018","journal-title":"Renew. Energy"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1016\/j.rser.2019.01.014","article-title":"Significant wave height forecasting via an extreme learning machine model integrated with improved complete ensemble empirical mode decomposition","volume":"104","author":"Ali","year":"2019","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_25","unstructured":"Momin, A.M., Ahmad, I., and Islam, M. (2007, January 29\u201331). Weed Classification Using Two Dimensional Weed Coverage Rate (2D-WCR) for Real-Time Selective Herbicide Applications. Proceedings of the International Conference on Computing, Information and Systems Science and Engineering, Bangkok, Thailand."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2727","DOI":"10.1007\/s00521-017-3225-z","article-title":"Accurate photovoltaic power forecasting models using deep LSTM-RNN","volume":"31","author":"Mahmoud","year":"2019","journal-title":"Neural Comput. Appl."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zhang, J., Chi, Y., and Xiao, L. (2018, January 23\u201325). Solar power generation forecast based on LSTM. Proceedings of the 2018 IEEE 9th International Conference on Software Engineering and Service Science (ICSESS), Beijing, China.","DOI":"10.1109\/ICSESS.2018.8663788"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"105790","DOI":"10.1016\/j.ijepes.2019.105790","article-title":"Day-ahead photovoltaic power forecasting approach based on deep convolutional neural networks and meta learning","volume":"118","author":"Zang","year":"2020","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"3170","DOI":"10.1109\/JIOT.2020.3013306","article-title":"An efficient deep learning framework for intelligent energy management in IoT networks","volume":"8","author":"Han","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Habib, S., Alsanea, M., Aloraini, M., Al-Rawashdeh, H.S., Islam, M., and Khan, S. (2022). An Efficient and Effective Deep Learning-Based Model for Real-Time Face Mask Detection. Sensors, 22.","DOI":"10.3390\/s22072602"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"5195508","DOI":"10.1155\/2021\/5195508","article-title":"Vision sensor-based real-time fire detection in resource-constrained IoT environments","volume":"2021","author":"Yar","year":"2021","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"105403","DOI":"10.1016\/j.engappai.2022.105403","article-title":"Randomly Initialized CNN with Densely Connected Stacked Autoencoder for Efficient Fire Detection","volume":"116","author":"Khan","year":"2022","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"6331","DOI":"10.1109\/TIP.2022.3207006","article-title":"Optimized Dual Fire Attention Network and Medium-Scale Fire Classification Benchmark","volume":"31","author":"Yar","year":"2022","journal-title":"IEEE Trans. Image Process."},{"key":"ref_34","first-page":"21","article-title":"Fire Detection via Effective Vision Transformers","volume":"17","author":"Yar","year":"2021","journal-title":"J. Korean Inst. Next Gener. Comput."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Albattah, W., Habib, S., Alsharekh, M.F., Islam, M., Albahli, S., and Dewi, D.A. (2022). An Overview of the Current Challenges, Trends, and Protocols in the Field of Vehicular Communication. Electronics, 11.","DOI":"10.3390\/electronics11213581"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Gensler, A., Henze, J., Sick, B., and Raabe, N. (2016, January 9\u201312). Deep Learning for solar power forecasting\u2014An approach using AutoEncoder and LSTM Neural Networks. Proceedings of the 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Budapest, Hungary.","DOI":"10.1109\/SMC.2016.7844673"},{"key":"ref_37","unstructured":"Sorkun, M.C., Paoli, C., and Incel, \u00d6.D. (December, January 30). Time series forecasting on solar irradiation using deep learning. Proceedings of the 2017 10th International Conference on Electrical and Electronics Engineering (ELECO), Bursa, Turkey."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"101815","DOI":"10.1016\/j.jksus.2021.101815","article-title":"Boosting energy harvesting via deep learning-based renewable power generation prediction","volume":"34","author":"Khan","year":"2022","journal-title":"J. King Saud Univ.-Sci."},{"key":"ref_39","unstructured":"Dey, S., Pratiher, S., Banerjee, S., and Mukherjee, C.K. (2017). Solarisnet: A deep regression network for solar radiation prediction. arXiv."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Yan, K., Shen, H., Wang, L., Zhou, H., Xu, M., and Mo, Y. (2020). Short-term solar irradiance forecasting based on a hybrid deep learning methodology. Information, 11.","DOI":"10.3390\/info11010032"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"105411","DOI":"10.1016\/j.ijepes.2019.105411","article-title":"A novel convolutional neural network framework based solar irradiance prediction method","volume":"114","author":"Dong","year":"2020","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1016\/j.enbuild.2019.04.034","article-title":"Recurrent inception convolution neural network for multi short-term load forecasting","volume":"194","author":"Kim","year":"2019","journal-title":"Energy Build."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"143759","DOI":"10.1109\/ACCESS.2020.3009537","article-title":"A novel CNN-GRU-based hybrid approach for short-term residential load forecasting","volume":"8","author":"Sajjad","year":"2020","journal-title":"IEEE Access"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"120996","DOI":"10.1016\/j.energy.2021.120996","article-title":"Day-ahead hourly photovoltaic power forecasting using attention-based CNN-LSTM neural network embedded with multiple relevant and target variables prediction pattern","volume":"232","author":"Qu","year":"2021","journal-title":"Energy"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Khan, Z.A., Hussain, T., Ullah, A., Rho, S., Lee, M., and Baik, S.W. (2020). Towards efficient electricity forecasting in residential and commercial buildings: A novel hybrid CNN with a LSTM-AE based framework. Sensors, 20.","DOI":"10.3390\/s20051399"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Wang, F., Yu, Y., Zhang, Z., Li, J., Zhen, Z., and Li, K. (2018). Wavelet decomposition and convolutional LSTM networks based improved deep learning model for solar irradiance forecasting. Appl. Sci., 8.","DOI":"10.3390\/app8081286"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Khan, Z.A., Ullah, A., Ullah, W., Rho, S., Lee, M., and Baik, S.W. (2020). Electrical energy prediction in residential buildings for short-term horizons using hybrid deep learning strategy. Appl. Sci., 10.","DOI":"10.3390\/app10238634"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"116225","DOI":"10.1016\/j.energy.2019.116225","article-title":"Photovoltaic power forecasting based LSTM-Convolutional Network","volume":"189","author":"Wang","year":"2019","journal-title":"Energy"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"510","DOI":"10.1016\/j.solener.2019.01.096","article-title":"3D-CNN-based feature extraction of ground-based cloud images for direct normal irradiance prediction","volume":"181","author":"Zhao","year":"2019","journal-title":"Sol. Energy"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"839","DOI":"10.1016\/j.enconman.2018.06.017","article-title":"Association rule mining based quantitative analysis approach of household characteristics impacts on residential electricity consumption patterns","volume":"171","author":"Wang","year":"2018","journal-title":"Energy Convers. Manag."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Ullah, W., Ullah, A., Hussain, T., Khan, Z.A., and Baik, S.W. (2021). An efficient anomaly recognition framework using an attention residual LSTM in surveillance videos. Sensors, 21.","DOI":"10.3390\/s21082811"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"109456","DOI":"10.1016\/j.knosys.2022.109456","article-title":"Intelligent dual stream CNN and echo state network for anomaly detection","volume":"253","author":"Ullah","year":"2022","journal-title":"Knowl.-Based Syst."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"6354","DOI":"10.1016\/j.egyr.2021.09.080","article-title":"Condition monitoring and performance forecasting of wind turbines based on denoising autoencoder and novel convolutional neural networks","volume":"7","author":"Jia","year":"2021","journal-title":"Energy Rep."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"96065","DOI":"10.1109\/ACCESS.2020.2992283","article-title":"Application of Internet of Things and virtual reality technology in college physical education","volume":"8","author":"Ding","year":"2020","journal-title":"IEEE Access"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Chen, B., Lin, P., Lai, Y., Cheng, S., Chen, Z., and Wu, L. (2020). Very-short-term power prediction for PV power plants using a simple and effective RCC-LSTM model based on short term multivariate historical datasets. Electronics, 9.","DOI":"10.3390\/electronics9020289"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"35is9","DOI":"10.1016\/j.jclepro.2019.04.331","article-title":"Renewable energy prediction: A novel short-term prediction model of photovoltaic output power","volume":"228","author":"Li","year":"2019","journal-title":"J. Clean. Prod."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"113315","DOI":"10.1016\/j.apenergy.2019.113315","article-title":"A comparison of day-ahead photovoltaic power forecasting models based on deep learning neural network","volume":"251","author":"Wang","year":"2019","journal-title":"Appl. Energy"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"117894","DOI":"10.1016\/j.energy.2020.117894","article-title":"Prediction of photovoltaic power output based on similar day analysis, genetic algorithm and extreme learning machine","volume":"204","author":"Zhou","year":"2020","journal-title":"Energy"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1593","DOI":"10.1109\/TSTE.2021.3057521","article-title":"Multi-meteorological-factor-based graph modeling for photovoltaic power forecasting","volume":"12","author":"Cheng","year":"2021","journal-title":"IEEE Trans. Sustain. Energy"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"117410","DOI":"10.1016\/j.apenergy.2021.117410","article-title":"SolarNet: A hybrid reliable model based on convolutional neural network and variational mode decomposition for hourly photovoltaic power forecasting","volume":"300","author":"Korkmaz","year":"2021","journal-title":"Appl. Energy"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/2\/945\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:05:31Z","timestamp":1760119531000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/2\/945"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,13]]},"references-count":60,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["s23020945"],"URL":"https:\/\/doi.org\/10.3390\/s23020945","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,13]]}}}