{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,11]],"date-time":"2025-12-11T07:44:29Z","timestamp":1765439069029,"version":"build-2065373602"},"reference-count":90,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T00:00:00Z","timestamp":1760486400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MAKE"],"abstract":"<jats:p>Forecasting day-ahead electricity prices is a crucial research area. Both wholesale and retail sectors highly value improved forecast accuracy. Renewable energy sources have grown more influential and effective in the US power market. However, current forecasting models have shortcomings, including inadequate consideration of renewable energy impacts and insufficient feature selection. Many studies lack reproducibility, clear presentation of input features, and proper integration of renewable resources. This study addresses these gaps by incorporating a comprehensive set of input features, while these features are engineered to capture complex market dynamics. The model\u2019s unique aspect is its inclusion of renewable-related inputs, such as temperature data for solar energy effects and wind speed for wind energy impacts on US electricity prices. The research also employs data preprocessing techniques like windowing, cleaning, normalization, and feature engineering to enhance input data quality and relevance. We developed four advanced hybrid deep learning models to improve electricity price prediction accuracy and reliability. Our approach combines variational mode decomposition (VMD) with four deep learning (DL) architectures: dense neural networks (DNNs), convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and bidirectional LSTM (BiLSTM) networks. This integration aims to capture complex patterns and time-dependent relationships in electricity price data. Among these, the VMD-BiLSTM model consistently outperformed the others across all window implementations. Using 24 input features, this model achieved a remarkably low mean absolute error of 0.2733 when forecasting prices in the MISO market. Our research advances electricity price forecasting, particularly for the US energy market. These hybrid deep neural network models provide valuable tools and insights for market participants, energy traders, and policymakers.<\/jats:p>","DOI":"10.3390\/make7040120","type":"journal-article","created":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T14:04:02Z","timestamp":1760537042000},"page":"120","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Hybrid Deep Learning Approaches for Accurate Electricity Price Forecasting: A Day-Ahead US Energy Market Analysis with Renewable Energy"],"prefix":"10.3390","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7247-5564","authenticated-orcid":false,"given":"Md. Saifur","family":"Rahman","sequence":"first","affiliation":[{"name":"Department of Biostatistics and Data Science, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX 77030, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7593-9624","authenticated-orcid":false,"given":"Hassan","family":"Reza","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Computer Science, University of North Dakota, Grand Forks, ND 58201, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1109\/TSP.2013.2288675","article-title":"Variational Mode Decomposition","volume":"62","author":"Dragomiretskiy","year":"2014","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"115503","DOI":"10.1016\/j.apenergy.2020.115503","article-title":"Short-term electricity price and load forecasting in isolated power grids based on composite neural network and gravitational search optimization algorithm","volume":"277","author":"Heydari","year":"2020","journal-title":"Appl. Energy"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1016\/j.eneco.2014.09.007","article-title":"Aviation fuel demand development in China","volume":"46","author":"Chai","year":"2014","journal-title":"Energy Econ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"890","DOI":"10.1016\/j.apenergy.2017.11.098","article-title":"Forecasting day-ahead electricity prices in Europe: The importance of considering market integration","volume":"211","author":"Lago","year":"2018","journal-title":"Appl. Energy"},{"key":"ref_5","unstructured":"(2022, May 05). U.S. Electricity Grid & Markets, Available online: https:\/\/www.epa.gov\/green-power-markets\/us-electricity-grid-markets."},{"key":"ref_6","unstructured":"Hoff, S. (2016, July 20). U.S. Electric System is Made Up of Interconnections and Balancing Authorities, Available online: https:\/\/www.Eia.Gov\/Todayinenergy\/Detail.Php?Id=27152."},{"key":"ref_7","unstructured":"Fasching, E. (2022, September 09). In the First Half of 2022, 24% of U.S. Electricity Generation Came from Renewable Sources, Available online: https:\/\/www.Eia.Gov\/Todayinenergy\/Detail.Php?Id=53779#:~:Text=In%20the%20first%20half%20of,Generation%20came%20from%20renewable%20sources&text=In%20the%20first%20six%20months,From%20our%20Electric%20Power%20Monthly."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"474","DOI":"10.1016\/j.renene.2016.03.053","article-title":"The impact of wind power on electricity prices","volume":"94","author":"Brinkman","year":"2016","journal-title":"Renew. Energy"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1016\/j.techfore.2019.01.006","article-title":"Robust forecasting of electricity prices: Simulations, models and the impact of renewable sources","volume":"141","author":"Grossi","year":"2019","journal-title":"Technol. Forecast. Soc. Change"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"104532","DOI":"10.1016\/j.eneco.2019.104532","article-title":"Assessing the impact of renewable energy sources on the electricity price level and variability\u2014A quantile regression approach","volume":"85","author":"Maciejowska","year":"2020","journal-title":"Energy Econ."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Singh, N., Hussain, S., and Tiwari, S. (2018). A PSO-based ANN model for short-term electricity price forecasting. Advances in Intelligent Systems and Computing, Springer Nature.","DOI":"10.1007\/978-981-10-7386-1_47"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Aggarwal, A., and Tripathi, M.M. (2017, January 5\u20137). A novel hybrid approach using wavelet transform, time series time delay neural network, and error predicting algorithm for day-ahead electricity price forecasting. Proceedings of the International Conference on Computer Applications in Electrical Engineering-Recent Advances, Roorkee, India.","DOI":"10.1109\/CERA.2017.8343326"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1952","DOI":"10.1002\/etep.1949","article-title":"Short-term LMP forecasting using an artificial neural network incorporating empirical mode decomposition","volume":"25","author":"Hong","year":"2014","journal-title":"Int. Trans. Electr. Energy Syst."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Talari, S., Shafie-khah, M., Os\u00f3rio, G., Wang, F., Heidari, A., and Catal\u00e3o, J. (2017). Price forecasting of electricity markets in the presence of high penetration of wind power generators. Sustainability, 9.","DOI":"10.3390\/su9112065"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.energy.2017.02.094","article-title":"Short-term electricity price forecast based on an environmentally adapted generalized neuron","volume":"125","author":"Singh","year":"2017","journal-title":"Energy"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Dai, R., Liu, G., Wang, Z., and Lu, S. (2018, January 21\u201323). Power market price forecasting via deep learning. Proceedings of the 44th Annual Conference of the IEEE Industrial Electronics Society, Washington, DC, USA.","DOI":"10.1109\/IECON.2018.8591581"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.apenergy.2017.10.058","article-title":"A bat-optimized neural network and wavelet transform approach for short-term price forecasting","volume":"210","author":"Bento","year":"2018","journal-title":"Appl. Energy"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1080\/01430750.2016.1269674","article-title":"Electricity price forecasting using neural networks with an improved iterative training algorithm","volume":"39","author":"Khajeh","year":"2017","journal-title":"Int. J. Ambient. Energy"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"115873","DOI":"10.1016\/j.energy.2019.115873","article-title":"Multi-agent microgrid energy management based on deep learning forecaster","volume":"186","author":"Afrasiabi","year":"2019","journal-title":"Energy"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"390","DOI":"10.1016\/j.apenergy.2016.12.134","article-title":"Multi-step ahead electricity price forecasting using a hybrid model based on two-layer decomposition technique and BP neural network optimized by the firefly algorithm","volume":"190","author":"Wang","year":"2017","journal-title":"Appl. Energy"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Jiang, P., Ma, X., and Liu, F. (2015). A new hybrid model based on data preprocessing and an intelligent optimization algorithm for electrical power system forecasting. Math. Probl. Eng., 2015.","DOI":"10.1155\/2015\/815253"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1016\/j.ijepes.2018.08.025","article-title":"Forecasting day-ahead electricity prices using a new integrated model","volume":"105","author":"Zhang","year":"2019","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Dastile, X., Celik, T., and Potsane, M. (2020). Statistical and machine learning models in credit scoring: A systematic literature survey. Appl. Soft Comput. J., 91.","DOI":"10.1016\/j.asoc.2020.106263"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1030","DOI":"10.1016\/j.ijforecast.2014.08.008","article-title":"Electricity price forecasting: A review of the state-of-the-art with a look into the future","volume":"30","author":"Weron","year":"2014","journal-title":"Int. J. Forecast."},{"key":"ref_25","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_26","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/j.eneco.2015.05.014","article-title":"Forecasting day-ahead electricity prices: Utilizing hourly prices","volume":"50","author":"Raviv","year":"2015","journal-title":"Energy Econ."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Agga, A., Abbou, A., Labbadi, M., and Houm, Y.E. (2021, January 17\u201320). Short-Term Load Forecasting: Based on Hybrid CNN-LSTM Neural Network. Proceedings of the 2021 6th International Conference on Power and Renewable Energy (ICPRE), Shanghai, China.","DOI":"10.1109\/ICPRE52634.2021.9635488"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"114087","DOI":"10.1016\/j.apenergy.2019.114087","article-title":"An adaptive hybrid model for short term electricity price forecasting","volume":"258","author":"Zhang","year":"2020","journal-title":"Appl. Energy"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"499","DOI":"10.3390\/forecast5030028","article-title":"A Hybrid Model for Multi-Day-Ahead Electricity Price Forecasting considering Price Spikes","volume":"5","author":"Jaimes","year":"2023","journal-title":"Forecasting"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Acaro\u011flu, H., and M\u00e1rquez, F.P.G. (2021). Comprehensive Review on Electricity Market Price and Load Forecasting Based on Wind Energy. Energies, 14.","DOI":"10.3390\/en14227473"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1014","DOI":"10.1109\/TPWRS.2002.804943","article-title":"ARIMA models to predict next-day electricity prices","volume":"18","author":"Contreras","year":"2003","journal-title":"IEEE Trans. Power Syst."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1035","DOI":"10.1109\/TPWRS.2005.846054","article-title":"Day-Ahead Electricity Price Forecasting Using the Wavelet Transform and ARIMA Models","volume":"20","author":"Conejo","year":"2005","journal-title":"IEEE Trans. Power Syst."},{"key":"ref_33","unstructured":"Shivanipickl (2022, November 02). Mastering Data: Exploring 5 Statistical Data Analysis Techniques with Real-World Examples. Available online: https:\/\/medium.com\/@shivanipickl\/mastering-data-exploring-5-statistical-data-analysis-techniques-with-real-world-examples-5e9064be6d46."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Uniejewski, B., Nowotarski, J., and Weron, R. (2016). Automated variable selection and shrinkage for day-ahead electricity price forecasting. Energies, 9.","DOI":"10.3390\/en9080621"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1016\/j.apenergy.2018.02.069","article-title":"Forecasting spot electricity prices: Deep learning approaches and empirical comparison of traditional algorithms","volume":"221","author":"Lago","year":"2018","journal-title":"Appl. Energy"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"396","DOI":"10.1016\/j.eneco.2017.12.016","article-title":"Day-ahead electricity price forecasting with high-dimensional structures: Univariate vs. multivariate modeling frameworks","volume":"70","author":"Ziel","year":"2018","journal-title":"Energy Econ."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"430","DOI":"10.1016\/j.eneco.2015.08.005","article-title":"Forecasting day ahead electricity spot prices: The impact of the EXAA to other European electricity markets","volume":"51","author":"Ziel","year":"2015","journal-title":"Energy Econ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"4977","DOI":"10.1109\/TPWRS.2016.2521545","article-title":"Forecasting electricity spot prices using lasso: On capturing the autoregressive intraday structure","volume":"31","author":"Ziel","year":"2016","journal-title":"IEEE Trans. Power Syst."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Uniejewski, B., and Weron, R. (2018). Efficient forecasting of electricity spot prices with expert and LASSO models. Energies, 11.","DOI":"10.3390\/en11082039"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1533","DOI":"10.1016\/j.ijforecast.2019.02.001","article-title":"Understanding intraday electricity markets: Variable selection and very short-term price forecasting using LASSO","volume":"35","author":"Uniejewski","year":"2019","journal-title":"Int. J. Forecast."},{"key":"ref_41","first-page":"4","article-title":"Fundamental and behavioural drivers of electricity price volatility","volume":"14","author":"Karakatsani","year":"2010","journal-title":"Stud. Nonlinear Dyn. Econom."},{"key":"ref_42","first-page":"938","article-title":"State-of-the-art in artificial neural network applications: A survey","volume":"4","author":"Jantan","year":"2018","journal-title":"Heliyon"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Rahman, M.S. (2023). A Hybrid Deep Neural Network Model to Forecast Day-Ahead Electricity Prices in the USA Energy Market. [Ph.D. Thesis, University of North Dakota]. Available online: https:\/\/commons.und.edu\/theses\/5330.","DOI":"10.1109\/AIIoT58121.2023.10174342"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2673","DOI":"10.1109\/TPWRS.2016.2628873","article-title":"Short-Term Electricity Price Forecasting With Stacked Denoising Autoencoders","volume":"Volume 32","author":"Wang","year":"2017","journal-title":"IEEE Transactions on Power Systems"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Chen, Y., Wang, Y., Ma, J., and Jin, Q. (2019). BRIM: An accurate electricity spot price prediction scheme-based bidirectional recurrent neural network and integrated market. Energies, 12.","DOI":"10.3390\/en12122241"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"108161","DOI":"10.1109\/ACCESS.2019.2932999","article-title":"An optimized heterogeneous structure LSTM network for electricity price forecasting","volume":"7","author":"Zhou","year":"2019","journal-title":"IEEE Access"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Mujeeb, S., Javaid, N., Ilahi, M., Wadud, Z., Ishmanov, F., and Afzal, M. (2019). Deep long short term memory: A new price and load forecasting scheme for big data in smart cities. Sustainability, 11.","DOI":"10.3390\/su11040987"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Xu, J., and Baldick, R. (2019, January 25\u201328). Day-ahead price forecasting in ERCOT market using neural network approaches. Proceedings of the Tenth ACM International Conference on Future Energy Systems, Phoenix, AZ, USA.","DOI":"10.1145\/3307772.3331024"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Meier, J.-H., Schneider, S., Schmidt, I., Sch\u00fcller, P., Sch\u00f6nfeldt, T., and Wanke, B. (2019). ANN-based electricity price forecasting under special consideration of time series properties. Information and Communication Technologies in Education, Research, and Industrial Applications, Springer International Publishing.","DOI":"10.1007\/978-3-030-13929-2_13"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Chang, Z., Zhang, Y., and Chen, W. (2018, January 23\u201325). Effective Adam-optimized LSTM neural network for electricity price forecasting. Proceedings of the 2018 IEEE International Conference on Software Engineering and Service Science, Beijing, China.","DOI":"10.1109\/ICSESS.2018.8663710"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Chinnathambi, R.A., Plathottam, S.J., Hossen, T., Nair, A.S., and Ranganathan, P. (2018, January 10\u201311). Deep neural networks (DNN) for day-ahead electricity price markets. Proceedings of the 2018 IEEE Electrical Power and Energy Conference, Toronto, ON, Canada.","DOI":"10.1109\/EPEC.2018.8598327"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1497","DOI":"10.1016\/j.apenergy.2019.03.129","article-title":"A two-stage supervised learning approach for electricity price forecasting by leveraging different data sources","volume":"242","author":"Luo","year":"2019","journal-title":"Appl. Energy"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Atef, S., and Eltawil, A.B. (2019, January 12\u201315). A comparative study using deep learning and support vector regression for electricity price forecasting in smart grids. Proceedings of the 2019 IEEE International Conference on Industrial Engineering and Applications, Tokyo, Japan.","DOI":"10.1109\/IEA.2019.8715213"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"115804","DOI":"10.1016\/j.energy.2019.07.134","article-title":"Electricity price prediction based on a hybrid model of Adam optimized LSTM neural network and wavelet transform","volume":"187","author":"Chang","year":"2019","journal-title":"Energy"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Zahid, M., Ahmed, F., Javaid, N., Abbasi, R., Zainab Kazmi, H., Javaid, A., Bilal, M., Akbar, M., and Ilahi, M. (2019). Electricity price and load forecasting using enhanced convolutional neural network and enhanced support vector regression in smart grids. Electronics, 8.","DOI":"10.3390\/electronics8020122"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"2369","DOI":"10.1109\/TII.2019.2933009","article-title":"A novel electricity price forecasting approach based on dimension reduction strategy and rough artificial neural networks","volume":"16","author":"Jahangir","year":"2019","journal-title":"IEEE Trans. Ind. Inf."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Ahmad, W., Javaid, N., Chand, A., Shah, S.Y.R., Yasin, U., Khan, M., and Syeda, A. (2019). Electricity price forecasting in smart grid: A novel E-CNN model. Web, Artificial Intelligence and Network Applications, Springer International Publishing.","DOI":"10.1007\/978-3-030-15035-8_109"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Aineto, D., Iranzo-S\u00e1nchez, J., Lemus-Z\u00fa\u00f1iga, L.G., Onaindia, E., and Urchuegu\u00eda, J.F. (2019). On the influence of renewable energy sources in electricity price forecasting in the Iberian market. Energies, 12.","DOI":"10.3390\/en12112082"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1016\/j.epsr.2018.09.004","article-title":"Hybrid model using a three-stage algorithm for simultaneous load and price forecasting","volume":"165","author":"Nazar","year":"2018","journal-title":"Electr. Power Syst. Res."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1016\/j.apenergy.2016.12.130","article-title":"Electricity price forecasting by a hybrid model, combining wavelet transform, ARMA, and kernel-based extreme learning machine methods","volume":"190","author":"Yang","year":"2017","journal-title":"Appl. Energy"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Victoire, A.A., Gobu, B., Jaikumar, S., Arulmozhi, N., Kanimozhi, P., and Victoire, A. (2018, January 17\u201320). Two-stage machine learning framework for simultaneous forecasting of price-load in the smart grid. Proceedings of the 2018 IEEE International Conference on Machine Learning and Applications, Orlando, FL, USA.","DOI":"10.1109\/ICMLA.2018.00176"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1907","DOI":"10.1109\/JSYST.2015.2487339","article-title":"Comparing variational and empirical mode decomposition in forecasting day-ahead energy prices","volume":"11","author":"Lahmiri","year":"2017","journal-title":"IEEE Syst. J."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"77674","DOI":"10.1109\/ACCESS.2019.2922420","article-title":"Short-term electricity price forecasting via hybrid backtracking search algorithm and ANFIS approach","volume":"7","author":"Pourdaryaei","year":"2019","journal-title":"IEEE Access"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"642","DOI":"10.1016\/j.enconman.2015.08.025","article-title":"Electricity price forecast using Combinatorial Neural Network trained by a new stochastic search method","volume":"105","author":"Abedinia","year":"2015","journal-title":"Energy Convers. Manag."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"1457","DOI":"10.1007\/s00521-018-3652-5","article-title":"Short-term electricity price forecasting and classification in smart grids using optimized multi kernel extreme learning machine","volume":"32","author":"Bisoi","year":"2018","journal-title":"Neural Comput. Appl."},{"key":"ref_66","unstructured":"Saifur, R.M., Reza, H., and Kim, E. (2023, January 7\u201310). A Hybrid Deep Neural Network Model to Forecast Day-Ahead Electricity Prices in the USA Energy Market. Proceedings of the 2023 IEEE World AI IoT Congress (AIIoT), Seattle, WA, USA."},{"key":"ref_67","first-page":"457","article-title":"Hybrid and Ensemble Methods in Machine Learning, J. UCS Special Issue","volume":"19","author":"Kazienko","year":"2013","journal-title":"J. Univers. Comput. Sci."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"106995","DOI":"10.1016\/j.epsr.2020.106995","article-title":"Short-term electricity load and price forecasting by a new optimal LSTM-NN based prediction algorithm","volume":"192","author":"Memarzadeh","year":"2021","journal-title":"Electr. Power Syst. Res."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1142\/S1793536909000047","article-title":"Ensemble Empirical Mode Decomposition: A Noise-Assisted Data Analysis Method","volume":"1","author":"Wu","year":"2009","journal-title":"Adv. Data Sci. Adapt. Anal."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Sezer, O.B., Gudelek, M.U., and Ozbayoglu, A.M. (2019). Financial Time Series Forecasting with Deep Learning: A Systematic Literature Review: 2005\u20132019. arXiv.","DOI":"10.1016\/j.asoc.2020.106181"},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Lago, J., Marcjasz, G., De Schutter, B., and Weron, R. (2020). Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices, and an open-access benchmark. arXiv.","DOI":"10.1016\/j.apenergy.2021.116983"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"Lecun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Dwivedi, S.A., Attry, A., Parekh, D., and Singla, K. (2021, January 19\u201320). Analysis and forecasting of Time-Series data using S-ARIMA, CNN and LSTM. Proceedings of the 2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS), Greater Noida, India.","DOI":"10.1109\/ICCCIS51004.2021.9397134"},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Bahri, M.Z., and Vahidnia, S. (2022, January 16\u201318). Time Series Forecasting Using Smoothing Ensemble Empirical Mode Decomposition and Machine Learning Techniques. Proceedings of the 2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), Maldives, Maldives.","DOI":"10.1109\/ICECCME55909.2022.9988336"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Selvin, S., Vinayakumar, R., Gopalakrishnan, E.A., Menon, V.K., and Soman, K.P. (2017, January 13\u201316). Stock price prediction using LSTM, RNN and CNN-sliding window model. Proceedings of the 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Udupi, India.","DOI":"10.1109\/ICACCI.2017.8126078"},{"key":"ref_77","unstructured":"Contributors, W. (2022, December 11). Long Short-Term Memory. Available online: https:\/\/en.wikipedia.org\/w\/index.php?title=Long_short-term_memory&oldid=1005032489."},{"key":"ref_78","unstructured":"Phi, M. (2022, December 10). Illustrated Guide to LSTM\u2019s and GRU\u2019s: A Step By-Step Explanation. Available online: https:\/\/towardsdatascience.com\/illustrated-guide-to-lstms-and-gru-s-a-step-by-step-explanation-44e9eb85bf21."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"2673","DOI":"10.1109\/78.650093","article-title":"Bidirectional recurrent neural networks","volume":"45","author":"Schuster","year":"1997","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Chen, G., Zhang, D., Xiang, W., Guan, Z., and Huang, J. (2022, January 16\u201318). Power Load Forecasting Based on COA-Bi-LSTM Method. Proceedings of the 2022 2nd International Conference on Electrical Engineering and Control Science (IC2ECS), Nanjing, China.","DOI":"10.1109\/IC2ECS57645.2022.10088113"},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Sharma, U., and Sharma, C. (2022, January 27\u201328). Deep Learning Based Prediction of Weather Using Hybrid_Stacked Bi-Long Short Term Memory. Proceedings of the 2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence), Noida, India.","DOI":"10.1109\/Confluence52989.2022.9734133"},{"key":"ref_82","unstructured":"Midcontinent Independent System Operator, Inc (2022, December 11). Historical Annual Day-Ahead LMPs (zip). Available online: https:\/\/www.Misoenergy.Org\/Markets-and-Operations\/Real-Time--Market-Data\/Market-Reports\/#nt=%2FMarketReportType%3AHistorical%20LMP%2FMarketReportName%3AHistorical%20Annual%20Day-Ahead%20LMPs%20(Zip)&t=10&p=0&s=MarketReportPublished&sd=desc."},{"key":"ref_83","unstructured":"LCG Consulting (2022, December 11). MISO (Midwest Independent Transmission System Operator) Day-Ahead Energy Price. Available online: http:\/\/Energyonline.Com\/Data\/GenericData.Aspx?DataId=9&MISO___Day-Ahead_Energy_Price."},{"key":"ref_84","unstructured":"Iowa AWOS (2022, December 11). Automated Airport Weather Observations from Around the World. Available online: https:\/\/mesonet.agron.iastate.edu\/request\/download.phtml."},{"key":"ref_85","unstructured":"Wikipedia.org (2023, July 06). Spline Interpolation. Available online: https:\/\/en.wikipedia.org\/wiki\/Spline_interpolation."},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Patro, S.G., and Sahu, K.K. (2015). Normalization: A Preprocessing Stage. arXiv.","DOI":"10.17148\/IARJSET.2015.2305"},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Al Machot, F., Mayr, H.C., and Ranasinghe, S. (2016, January 5\u20138). A windowing approach for activity recognition in sensor data streams. Proceedings of the 2016 Eighth International Conference on Ubiquitous and Future Networks (ICUFN), Vienna, Austria.","DOI":"10.1109\/ICUFN.2016.7536937"},{"key":"ref_88","unstructured":"tensorflow.org (2022, September 29). Colab\u2019s \u2018Pay As You Go\u2019 Offers More Access to Powerful NVIDIA Compute for Machine Learning. Available online: https:\/\/blog.tensorflow.org\/2022\/09\/colabs-pay-as-you-go-offers-more-access-to-powerful-nvidia-compute-for-machine-learning.html."},{"key":"ref_89","unstructured":"Wikipedia Contributors (2022, December 10). Mean Squared Error. Wikipedia.org, The Free Encyclopedia, Available online: https:\/\/en.wikipedia.org\/w\/index.php?title=Mean_squared_error&oldid=1020706752."},{"key":"ref_90","unstructured":"Wikipedia.org (2022, December 10). Mean Absolute Error. Available online: https:\/\/en.wikipedia.org\/wiki\/Mean_absolute_error."}],"container-title":["Machine Learning and Knowledge Extraction"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-4990\/7\/4\/120\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T14:31:13Z","timestamp":1760538673000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-4990\/7\/4\/120"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,15]]},"references-count":90,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["make7040120"],"URL":"https:\/\/doi.org\/10.3390\/make7040120","relation":{},"ISSN":["2504-4990"],"issn-type":[{"value":"2504-4990","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,15]]}}}