{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T06:38:38Z","timestamp":1781591918351,"version":"3.54.5"},"reference-count":82,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2022,6,12]],"date-time":"2022-06-12T00:00:00Z","timestamp":1654992000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Union Horizon 2020 research and innovation programme under the Marie Sklodowska\u2013Curie","award":["No. 754382"],"award-info":[{"award-number":["No. 754382"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper proposes a new hybrid framework for short-term load forecasting (STLF) by combining the Feature Engineering (FE) and Bayesian Optimization (BO) algorithms with a Bayesian Neural Network (BNN). The FE module comprises feature selection and extraction phases. Firstly, by merging the Random Forest (RaF) and Relief-F (ReF) algorithms, we developed a hybrid feature selector based on grey correlation analysis (GCA) to eliminate feature redundancy. Secondly, a radial basis Kernel function and principal component analysis (KPCA) are integrated into the feature-extraction module for dimensional reduction. Thirdly, the Bayesian Optimization (BO) algorithm is used to fine-tune the control parameters of a BNN and provides more accurate results by avoiding the optimal local trapping. The proposed FE-BNN-BO framework works in such a way to ensure stability, convergence, and accuracy. The proposed FE-BNN-BO model is tested on the hourly load data obtained from the PJM, USA, electricity market. In addition, the simulation results are also compared with other benchmark models such as Bi-Level, long short-term memory (LSTM), an accurate and fast convergence-based ANN (ANN-AFC), and a mutual-information-based ANN (ANN-MI). The results show that the proposed model has significantly improved the accuracy with a fast convergence rate and reduced the mean absolute percent error (MAPE).<\/jats:p>","DOI":"10.3390\/s22124446","type":"journal-article","created":{"date-parts":[[2022,6,13]],"date-time":"2022-06-13T02:01:44Z","timestamp":1655085704000},"page":"4446","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":62,"title":["Hyperparameter Optimization of Bayesian Neural Network Using Bayesian Optimization and Intelligent Feature Engineering for Load Forecasting"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1624-3045","authenticated-orcid":false,"given":"M.","family":"Zulfiqar","sequence":"first","affiliation":[{"name":"Department of Telecommunication Systems, Bahauddin Zakariya University, Multan 60000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4832-3373","authenticated-orcid":false,"given":"Kelum A. A.","family":"Gamage","sequence":"additional","affiliation":[{"name":"James Watt School of Engineering, James Watt South Building, University of Glasgow, Glasgow G12 8QQ, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"M.","family":"Kamran","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, University of Engineering and Technology, Lahore 54000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"M. B.","family":"Rasheed","sequence":"additional","affiliation":[{"name":"Escuela Polit\u00e9cnica Superior, Universidad de Alcal\u00e1, ISG, 28805 Alcal\u00e1 de Henares, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.apenergy.2014.03.045","article-title":"Improving photovoltaics grid integration through short time forecasting and self-consumption","volume":"125","author":"Matallanas","year":"2014","journal-title":"Appl. Energy"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Feinberg, E.A., and Genethliou, D. (2005). Load forecasting. Applied Mathematics for Restructured Electric Power Systems, Springer.","DOI":"10.1007\/0-387-23471-3_12"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1016\/j.ijepes.2014.07.029","article-title":"A hybrid model based on data preprocessing for electrical power forecasting","volume":"64","author":"Xiao","year":"2015","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Notton, G., and Voyant, C. (2018). Forecasting of intermittent solar energy resource. Advances in Renewable Energies and Power Technologies, Elsevier.","DOI":"10.1016\/B978-0-12-812959-3.00003-4"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"524","DOI":"10.1016\/j.energy.2015.01.063","article-title":"A combined model based on data pre-analysis and weight coefficients optimization for electrical load forecasting","volume":"82","author":"Xiao","year":"2015","journal-title":"Energy"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"270","DOI":"10.1016\/j.epsr.2017.01.035","article-title":"Short-term electric load forecasting based on singular spectrum analysis and support vector machine optimized by Cuckoo search algorithm","volume":"146","author":"Zhang","year":"2017","journal-title":"Electr. Power Syst. Res."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1016\/j.enconman.2012.08.001","article-title":"A novel economy reflecting short-term load forecasting approach","volume":"65","author":"Lin","year":"2013","journal-title":"Energy Conv. Manag."},{"key":"ref_8","first-page":"200","article-title":"Research on processing of short-term historical data of daily load based on Kalman filter","volume":"10","author":"Zhang","year":"2003","journal-title":"Power Syst. Technol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1109\/TPAS.1982.317242","article-title":"On-line load forecasting for energy control center application","volume":"PAS-101","author":"Irisarri","year":"1982","journal-title":"IEEE Trans. Power Appar. Syst."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1005","DOI":"10.1016\/j.ijforecast.2015.11.010","article-title":"GEFCom2014 probabilistic electric load forecasting using time series and semi-parametric regression models","volume":"32","author":"Dordonnat","year":"2016","journal-title":"In. J. Forecast."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"900","DOI":"10.1109\/TPAS.1971.293123","article-title":"Short-term load forecasting using general exponential smoothing","volume":"2","author":"Christiaanse","year":"1971","journal-title":"IEEE Trans. Power Appar. Syst."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Amral, N., Ozveren, C., and King, D. (2007, January 4\u20136). Short term load forecasting using multiple linear regression. Proceedings of the 2007 42nd International Universities Power Engineering Conference, Brighton, UK.","DOI":"10.1109\/UPEC.2007.4469121"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1016\/j.enpol.2012.05.026","article-title":"Application of residual modification approach in seasonal ARIMA for electricity demand forecasting: A case study of China","volume":"48","author":"Wang","year":"2012","journal-title":"Energy Policy"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3226","DOI":"10.1016\/j.apenergy.2010.04.006","article-title":"An enhanced radial basis function network for short-term electricity price forecasting","volume":"87","author":"Lin","year":"2010","journal-title":"Appl. Energy"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.apenergy.2016.01.050","article-title":"A combined model based on multiple seasonal patterns and modified firefly algorithm for electrical load forecasting","volume":"167","author":"Xiao","year":"2016","journal-title":"Appl. Energy"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"5962","DOI":"10.1016\/j.eswa.2008.07.030","article-title":"An expert system based on S-transform and neural network for automatic classification of power quality disturbances","volume":"36","author":"Uyar","year":"2009","journal-title":"Expert Syst. Appl."},{"key":"ref_17","unstructured":"Yang, J. (2006). Power System Short-Term Load Forecasting. [Ph.D. Thesis, Technical University]."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1104","DOI":"10.1016\/j.rser.2017.02.023","article-title":"A review and analysis of regression and machine learning models on commercial building electricity load forecasting","volume":"73","author":"Yildiz","year":"2017","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1016\/j.jpdc.2017.06.007","article-title":"An efficient deep model for day-ahead electricity load forecasting with stacked denoising auto-encoders","volume":"117","author":"Tong","year":"2018","journal-title":"J. Parallel Distrib. Comput."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1525","DOI":"10.1016\/S0196-8904(02)00148-6","article-title":"Artificial intelligence in short term electric load forecasting: A state-of-the-art survey for the researcher","volume":"44","author":"Metaxiotis","year":"2003","journal-title":"Energy Conv. Manag."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2757","DOI":"10.1016\/S0031-3203(03)00175-4","article-title":"Constructing support vector machine ensemble","volume":"36","author":"Kim","year":"2003","journal-title":"Pattern Recogn."},{"key":"ref_22","first-page":"603","article-title":"Bayesian backpropagation","volume":"5","author":"Buntine","year":"1991","journal-title":"Complex Syst."},{"key":"ref_23","unstructured":"MacKay, D.J., and Mac Kay, D.J. (2003). Information Theory, Inference and Learning Algorithms, Cambridge University Press."},{"key":"ref_24","unstructured":"Neal, R.M. (1992). Bayesian Training of Backpropagation Networks by the Hybrid Monte Carlo Method, University of Toronto. Technical Report."},{"key":"ref_25","unstructured":"Snoek, J., Larochelle, H., and Adams, R.P. (2012). Practical bayesian optimization of machine learning algorithms. Adv. Neural Inf. Process. Syst., 25."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"126526","DOI":"10.1016\/j.jhydrol.2021.126526","article-title":"A novel attention-based LSTM cell post-processor coupled with bayesian optimization for streamflow prediction","volume":"601","author":"Alizadeh","year":"2021","journal-title":"J. Hydrol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"102237","DOI":"10.1016\/j.scs.2020.102237","article-title":"A Lag-FLSTM deep learning network based on Bayesian Optimization for multi-sequential-variant PM2. 5 prediction","volume":"60","author":"Ma","year":"2020","journal-title":"Sustain. Cities Soc."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"110511","DOI":"10.1016\/j.chaos.2020.110511","article-title":"Prediction of COVID-19 confirmed cases combining deep learning methods and Bayesian optimization","volume":"142","author":"Abbasimehr","year":"2021","journal-title":"Chaos Solitons Fractals"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"121492","DOI":"10.1016\/j.energy.2021.121492","article-title":"A novel asynchronous deep reinforcement learning model with adaptive early forecasting method and reward incentive mechanism for short-term load forecasting","volume":"236","author":"Zhang","year":"2021","journal-title":"Energy"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.asoc.2018.02.004","article-title":"Optimal forecast combination based on neural networks for time series forecasting","volume":"66","author":"Wang","year":"2018","journal-title":"Appl. Soft Comput."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.ijforecast.2019.04.014","article-title":"The M4 Competition: 100,000 time series and 61 forecasting methods","volume":"36","author":"Makridakis","year":"2020","journal-title":"Int. J. Forecast."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Pelikan, M. (2005). Hierarchical Bayesian optimization algorithm. Hierarchical Bayesian Optimization Algorithm, Springer.","DOI":"10.1007\/b10910"},{"key":"ref_33","unstructured":"Khan, N., Goldberg, D.E., and Pelikan, M. (2002, January 9\u201313). Multi-objective Bayesian optimization algorithm. Proceedings of the 4th Annual Conference on Genetic and Evolutionary Computation, New York, NY, USA."},{"key":"ref_34","unstructured":"Schwarz, J., and Ocenasek, J. (2000, January 16\u201321). A problem knowledge-based evolutionary algorithm KBOA for hypergraph bisectioning. Proceedings of the 4th Joint Conference on Knowledge-Based Software Engineering, Brno, Czech Republic."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1109\/JSYST.2016.2594208","article-title":"A distributed short-term load forecasting method based on local weather information","volume":"12","author":"Liu","year":"2016","journal-title":"IEEE Syst. J."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"5271","DOI":"10.1109\/TSG.2017.2686012","article-title":"Deep learning for household load forecasting\u2014A novel pooling deep RNN","volume":"9","author":"Shi","year":"2017","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1087","DOI":"10.1109\/TPWRS.2017.2688178","article-title":"Short-term residential load forecasting based on resident behaviour learning","volume":"33","author":"Kong","year":"2017","journal-title":"IEEE Trans. Power Syst."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"2886","DOI":"10.1109\/TII.2017.2711648","article-title":"Hour-ahead price based energy management scheme for industrial facilities","volume":"13","author":"Huang","year":"2017","journal-title":"IEEE Trans. Ind. Inf."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1016\/j.solener.2018.06.103","article-title":"Probabilistic forecasting of solar power, electricity consumption and net load: Investigating the effect of seasons, aggregation and penetration on prediction intervals","volume":"171","author":"Munkhammar","year":"2018","journal-title":"Solar Energy"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"410","DOI":"10.1016\/j.enpol.2018.04.060","article-title":"Long term load forecasting accuracy in electric utility integrated resource planning","volume":"119","author":"Carvallo","year":"2018","journal-title":"Energy Policy"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"585","DOI":"10.1016\/j.ijforecast.2015.09.006","article-title":"Electric load forecasting with recency effect: A big data approach","volume":"32","author":"Wang","year":"2016","journal-title":"Int. J. Forecast."},{"key":"ref_42","unstructured":"Gavrilas, M. (2010). Heuristic and Metaheuristic Optimization Techniques with Application to Power Systems, Technical University of Iasi."},{"key":"ref_43","first-page":"137","article-title":"A survey of bio inspired optimization algorithms","volume":"2","author":"Binitha","year":"2012","journal-title":"Int. J. Soft Comput. Eng."},{"key":"ref_44","first-page":"143","article-title":"Efficient and robust parameter tuning for heuristic algorithms","volume":"24","author":"Akbaripour","year":"2013","journal-title":"Int. J. Ind. Eng. Prod. Res."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1352","DOI":"10.1016\/j.rser.2015.04.065","article-title":"A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings","volume":"50","author":"Raza","year":"2015","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.apenergy.2014.07.104","article-title":"A short-term load forecasting model of natural gas based on optimized genetic algorithm and improved BP neural network","volume":"134","author":"Yu","year":"2014","journal-title":"Appl. Energy"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"673","DOI":"10.1016\/j.ijepes.2014.04.014","article-title":"Hybrid improved differential evolution and wavelet neural network with load forecasting problem of air conditioning","volume":"61","author":"Liao","year":"2014","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"ref_48","first-page":"721","article-title":"Genetic algorithm-based non-linear auto-regressive with exogenous inputs neural network short-term and medium-term uncertainty modelling and prediction for electrical load and wind speed","volume":"2018","author":"Jawad","year":"2018","journal-title":"J. Eng."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"114405","DOI":"10.1016\/j.apenergy.2019.114405","article-title":"Robust and automatic data cleansing method for short-term load forecasting of distribution feeders","volume":"261","author":"Tindemans","year":"2020","journal-title":"Appl. Energy"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1078","DOI":"10.1016\/j.apenergy.2018.12.042","article-title":"Day-ahead building-level load forecasts using deep learning vs. traditional time-series techniques","volume":"236","author":"Cai","year":"2019","journal-title":"Appl. Energy"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.apenergy.2019.01.055","article-title":"A hybrid short-term load forecasting model based on variational mode decomposition and long short-term memory networks considering relevant factors with Bayesian optimization algorithm","volume":"237","author":"He","year":"2019","journal-title":"Appl. Energy"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"896","DOI":"10.1016\/j.apenergy.2019.01.046","article-title":"A hybrid model based on modified multi-objective cuckoo search algorithm for short-term load forecasting","volume":"237","author":"Wu","year":"2019","journal-title":"Appl. Energy"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.compeleceng.2017.07.006","article-title":"Smart grid load forecasting using online support vector regression","volume":"65","author":"Ezzeddine","year":"2018","journal-title":"Comput. Electr. Eng."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1016\/j.energy.2018.08.169","article-title":"Subsampled support vector regression ensemble for short term electric load forecasting","volume":"164","author":"Li","year":"2018","journal-title":"Energy"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.enbuild.2016.05.028","article-title":"Time series forecasting for building energy consumption using weighted Support Vector Regression with differential evolution optimization technique","volume":"126","author":"Zhang","year":"2016","journal-title":"Energy Build."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"734","DOI":"10.1016\/j.energy.2016.09.065","article-title":"Support vector regression with fruit fly optimization algorithm for seasonal electricity consumption forecasting","volume":"115","author":"Cao","year":"2016","journal-title":"Energy"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"6047","DOI":"10.1016\/j.eswa.2014.03.053","article-title":"A new hybrid modified firefly algorithm and support vector regression model for accurate short term load forecasting","volume":"41","author":"Samet","year":"2014","journal-title":"Exp. Syst. Appl."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/j.apenergy.2016.07.113","article-title":"Research and application of a hybrid model based on multi-objective optimization for electrical load forecasting","volume":"180","author":"Xiao","year":"2016","journal-title":"Appl. Energy"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1109\/TSG.2010.2078842","article-title":"Short-term load forecast of microgrids by a new bilevel prediction strategy","volume":"1","author":"Amjady","year":"2010","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Hafeez, G., Alimgeer, K.S., Wadud, Z., Shafiq, Z., Ali Khan, M.U., Khan, I., Khan, F.A., and Derhab, A. (2020). A novel accurate and fast converging deep learning-based model for electrical energy consumption forecasting in a smart grid. Energies, 13.","DOI":"10.3390\/en13092244"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/j.neucom.2020.05.075","article-title":"A support vector regression model hybridized with chaotic krill herd algorithm and empirical mode decomposition for regression task","volume":"410","author":"Zhang","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1016\/j.neucom.2017.01.090","article-title":"A switching delayed PSO optimized extreme learning machine for short-term load forecasting","volume":"240","author":"Zeng","year":"2017","journal-title":"Neurocomputing"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1016\/j.energy.2018.07.088","article-title":"Two stage forecast engine with feature selection technique and improved meta-heuristic algorithm for electricity load forecasting","volume":"161","author":"Ghadimi","year":"2018","journal-title":"Energy"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Shiri, A., Afshar, M., Rahimi-Kian, A., and Maham, B. (2015, January 17\u201319). Electricity price forecasting using Support Vector Machines by considering oil and natural gas price impacts. Proceedings of the 2015 IEEE International Conference on Smart Energy Grid Engineering (SEGE), Oshawa, ON, Canada.","DOI":"10.1109\/SEGE.2015.7324591"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"3341","DOI":"10.1109\/TSG.2016.2628061","article-title":"A short-term and high-resolution distribution system load forecasting approach using support vector regression with hybrid parameters optimization","volume":"9","author":"Jiang","year":"2016","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"298","DOI":"10.1016\/S0043-1648(03)00013-9","article-title":"Manufacturing process optimization for wear property of fiber-reinforced polybutylene terephthalate composites with grey relational analysis","volume":"254","author":"Fung","year":"2003","journal-title":"Wear"},{"key":"ref_67","first-page":"1","article-title":"Introduction to grey system theory","volume":"1","author":"Julong","year":"1989","journal-title":"J. Grey Syst."},{"key":"ref_68","unstructured":"Deng, J.L. (1990). A Course on Grey System Theory, Huazhong University of Science and Technology Press."},{"key":"ref_69","unstructured":"Deng, J. (1992). The Essential Methods of Grey Systems, Huazhong University of Science and Technology Press."},{"key":"ref_70","first-page":"63","article-title":"A Two-Stage Method for Classifiers Combination","volume":"1","author":"Kabir","year":"2008","journal-title":"Nashriyyah-i Muhandisi-i Barq va Muhandisi-i Kampyutar-i Iran"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1023\/A:1008202821328","article-title":"Differential evolution\u2013a simple and efficient heuristic for global optimization over continuous spaces","volume":"11","author":"Storn","year":"1997","journal-title":"J. Glob. Optim."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1109\/CCCM.2009.5268039","article-title":"Nonlinear system modeling based on KPCA and MKSVM","volume":"Volume 3","author":"Du","year":"2009","journal-title":"Proceedings of the 2009 ISECS International Colloquium on Computing, Communication, Control, and Management"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"777","DOI":"10.1016\/j.energy.2015.01.028","article-title":"A hybrid short-term load forecasting with a new input selection framework","volume":"81","author":"Ghofrani","year":"2015","journal-title":"Energy"},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Neill, S.P., and Hashemi, M.R. (2018). Fundamentals of Ocean Renewable Energy: Generating Electricity from the Sea, Academic Press.","DOI":"10.1016\/B978-0-12-810448-4.00010-0"},{"key":"ref_75","unstructured":"Woolf, B.P. (2010). Building Intelligent Interactive Tutors: Student-Centered Strategies for Revolutionizing e-Learning, Morgan Kaufmann."},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Fox, E.P. (1998). Data Analysis: A Bayesian Tutorial, OUP Oxford.","DOI":"10.2307\/1270652"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"448","DOI":"10.1162\/neco.1992.4.3.448","article-title":"A practical Bayesian framework for backpropagation networks","volume":"4","author":"MacKay","year":"1992","journal-title":"Neural Comput."},{"key":"ref_78","unstructured":"Ford, W. (2014). Numerical Linear Algebra with Applications: Using MATLAB, Academic Press."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"469","DOI":"10.1088\/0954-898X_6_3_011","article-title":"Probable networks and plausible predictions\u2014A review of practical Bayesian methods for supervised neural networks","volume":"6","author":"MacKay","year":"1995","journal-title":"Netw. Comput. Neural Syst."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Hafeez, G., Islam, N., Ali, A., Ahmad, S., Usman, M., and Saleem Alimgeer, K. (2019). A modular framework for optimal load scheduling under price-based demand response scheme in smart grid. Processes, 7.","DOI":"10.3390\/pr7080499"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"2587","DOI":"10.1109\/TII.2016.2638322","article-title":"An accurate and fast converging short-term load forecasting model for industrial applications in a smart grid","volume":"13","author":"Ahmad","year":"2016","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_82","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/12\/4446\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:28:23Z","timestamp":1760138903000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/12\/4446"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,12]]},"references-count":82,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2022,6]]}},"alternative-id":["s22124446"],"URL":"https:\/\/doi.org\/10.3390\/s22124446","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,12]]}}}