{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T11:49:19Z","timestamp":1740138559071,"version":"3.37.3"},"reference-count":28,"publisher":"Walter de Gruyter GmbH","issue":"2","funder":[{"DOI":"10.13039\/100000015","name":"U.S. Department of Energy","doi-asserted-by":"publisher","award":["FE0031547"],"award-info":[{"award-number":["FE0031547"]}],"id":[{"id":"10.13039\/100000015","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,2,25]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>This work presents an investigation into the ability of recurrent neural networks (RNNs) to provide long term predictions of time series data generated by coal fired power plants. While there are numerous studies which have used artificial neural networks (ANNs) to predict coal plant parameters, to the authors\u2019 knowledge these have almost entirely been restricted to predicting values at the next time step, and not farther into the future. Using a novel neuro-evolution strategy called Evolutionary eXploration of Augmenting Memory Models (EXAMM), we evolved RNNs with advanced memory cells to predict per-minute plant parameters and per-hour boiler parameters up to 8 hours into the future. These data sets were challenging prediction tasks as they involve spiking behavior in the parameters being predicted. While the evolved RNNs were able to successfully predict the spikes in the hourly data they did not perform very well in accurately predicting their severity. The per-minute data proved even more challenging as medium range predictions miscalculated the beginning and ending of spikes, and longer range predictions reverted to long term trends and ignored the spikes entirely. We hope this initial study will motivate further study into this highly challenging prediction problem. The use of fuel properties data generated by a new Coal Tracker Optimization (CTO) program was also investigated and this work shows that their use improved predictive ability of the evolved RNNs.<\/jats:p>","DOI":"10.1515\/auto-2019-0116","type":"journal-article","created":{"date-parts":[[2020,1,22]],"date-time":"2020-01-22T09:01:55Z","timestamp":1579683715000},"page":"130-139","source":"Crossref","is-referenced-by-count":1,"title":["Long term predictions of coal fired power plant data using evolved recurrent neural networks"],"prefix":"10.1515","volume":"68","author":[{"given":"Travis J.","family":"Desell","sequence":"first","affiliation":[{"name":"Golisano College of Computing and Information Sciences , 6925 Rochester Institute of Technology , Rochester , NY , USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"AbdElRahman A.","family":"ElSaid","sequence":"additional","affiliation":[{"name":"Golisano College of Computing and Information Sciences , 6925 Rochester Institute of Technology , Rochester , NY , USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zimeng","family":"Lyu","sequence":"additional","affiliation":[{"name":"Golisano College of Computing and Information Sciences , 6925 Rochester Institute of Technology , Rochester , NY , USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"David","family":"Stadem","sequence":"additional","affiliation":[{"name":"Microbeam Technologies Inc. , Grand Forks , ND , USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuchita","family":"Patwardhan","sequence":"additional","affiliation":[{"name":"Microbeam Technologies Inc. , Grand Forks , ND , USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Steve","family":"Benson","sequence":"additional","affiliation":[{"name":"Microbeam Technologies Inc. , Grand Forks , ND , USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"374","published-online":{"date-parts":[[2020,1,22]]},"reference":[{"key":"2023033110001594313_j_auto-2019-0116_ref_001_w2aab3b7b5b1b6b1ab1b7b1Aa","doi-asserted-by":"crossref","unstructured":"Michalis Mavrovouniotis and Shengxiang Yang. Evolving neural networks using ant colony optimization with pheromone trail limits. In 2013 13th UK Workshop on Computational Intelligence (UKCI), pages 16\u201323. IEEE, 2013.","DOI":"10.1109\/UKCI.2013.6651282"},{"key":"2023033110001594313_j_auto-2019-0116_ref_002_w2aab3b7b5b1b6b1ab1b7b2Aa","doi-asserted-by":"crossref","unstructured":"J Smrekar, D Pandit, Magnus Fast, Mohsen Assadi and Sudipta De. Prediction of power output of a coal-fired power plant by artificial neural network. Neural Computing and Applications, 19(5):725\u2013740, 2010.10.1007\/s00521-009-0331-6","DOI":"10.1007\/s00521-009-0331-6"},{"key":"2023033110001594313_j_auto-2019-0116_ref_003_w2aab3b7b5b1b6b1ab1b7b3Aa","doi-asserted-by":"crossref","unstructured":"Amrita Kumari, SK Das and PK Srivastava. Modeling fireside corrosion rate in a coal fired boiler using adaptive neural network formalism. Portugaliae Electrochimica Acta, 34(1):23\u201338, 2016.10.4152\/pea.201601023","DOI":"10.4152\/pea.201601023"},{"key":"2023033110001594313_j_auto-2019-0116_ref_004_w2aab3b7b5b1b6b1ab1b7b4Aa","doi-asserted-by":"crossref","unstructured":"Hao Zhou, Kefa Cen and Jianren Fan. Modeling and optimization of the NOx emission characteristics of a tangentially fired boiler with artificial neural networks. Energy, 29(1):167\u2013183, 2004.10.1016\/j.energy.2003.08.004","DOI":"10.1016\/j.energy.2003.08.004"},{"key":"2023033110001594313_j_auto-2019-0116_ref_005_w2aab3b7b5b1b6b1ab1b7b5Aa","doi-asserted-by":"crossref","unstructured":"Fang Wang, Suxia Ma, He Wang, Yaodong Li and Junjie Zhang. Prediction of NOx emission for coal-fired boilers based on deep belief network. Control Engineering Practice, 80:26\u201335, 2018.10.1016\/j.conengprac.2018.08.003","DOI":"10.1016\/j.conengprac.2018.08.003"},{"key":"2023033110001594313_j_auto-2019-0116_ref_006_w2aab3b7b5b1b6b1ab1b7b6Aa","doi-asserted-by":"crossref","unstructured":"Zhou Hao, Cen Kefa and Mao Jianbo. Combining neural network and genetic algorithms to optimize low no x pulverized coal combustion. Fuel, 80(15):2163\u20132169, 2001.10.1016\/S0016-2361(01)00104-1","DOI":"10.1016\/S0016-2361(01)00104-1"},{"key":"2023033110001594313_j_auto-2019-0116_ref_007_w2aab3b7b5b1b6b1ab1b7b7Aa","doi-asserted-by":"crossref","unstructured":"Jiyu Chen, Feng Hong, Mingming Gao, Taihua Chang and Liying Xu. Prediction model of scr outlet NOx based on LSTM algorithm. In Proceedings of the 2019 2nd International Conference on Intelligent Science and Technology, pages 7\u201310. ACM, 2019.","DOI":"10.1145\/3354142.3354144"},{"key":"2023033110001594313_j_auto-2019-0116_ref_008_w2aab3b7b5b1b6b1ab1b7b8Aa","doi-asserted-by":"crossref","unstructured":"Peng Tan, Biao He, Cheng Zhang, Debei Rao, Shengnan Li, Qingyan Fang and Gang Chen. Dynamic modeling of NOx emission in a 660 MW coal-fired boiler with long short-term memory. Energy, 176:429\u2013436, 2019.10.1016\/j.energy.2019.04.020","DOI":"10.1016\/j.energy.2019.04.020"},{"key":"2023033110001594313_j_auto-2019-0116_ref_009_w2aab3b7b5b1b6b1ab1b7b9Aa","doi-asserted-by":"crossref","unstructured":"Seyed Mostafa Safdarnejad, Jake\u2009F Tuttle and Kody\u2009M Powell. Dynamic modeling and optimization of a coal-fired utility boiler to forecast and minimize NOx and CO emissions simultaneously. Computers & Chemical Engineering, 124:62\u201379, 2019.10.1016\/j.compchemeng.2019.02.001","DOI":"10.1016\/j.compchemeng.2019.02.001"},{"key":"2023033110001594313_j_auto-2019-0116_ref_010_w2aab3b7b5b1b6b1ab1b7c10Aa","doi-asserted-by":"crossref","unstructured":"Cem Onat and Mahmut Daskin. A basic ann system for prediction of excess air coefficient on coal burners equipped with a ccd camera. Mathematics and Statistics, 7(1):1\u20139, 2019.10.13189\/ms.2019.070101","DOI":"10.13189\/ms.2019.070101"},{"key":"2023033110001594313_j_auto-2019-0116_ref_011_w2aab3b7b5b1b6b1ab1b7c11Aa","doi-asserted-by":"crossref","unstructured":"Peter\u2009J Brockwell, Richard\u2009A Davis and Stephen\u2009E Fienberg. Time Series: Theory and Methods: Theory and Methods. Springer Science & Business Media, 1991.","DOI":"10.1007\/978-1-4419-0320-4"},{"key":"2023033110001594313_j_auto-2019-0116_ref_012_w2aab3b7b5b1b6b1ab1b7c12Aa","doi-asserted-by":"crossref","unstructured":"Khalid Salama and Ashraf\u2009M Abdelbar. A novel ant colony algorithm for building neural network topologies. In Swarm Intelligence, pages 1\u201312. Springer, 2014.","DOI":"10.1007\/978-3-319-09952-1_1"},{"key":"2023033110001594313_j_auto-2019-0116_ref_013_w2aab3b7b5b1b6b1ab1b7c13Aa","doi-asserted-by":"crossref","unstructured":"Masanori Suganuma, Shinichi Shirakawa and Tomoharu Nagao. A genetic programming approach to designing convolutional neural network architectures. In Proceedings of the Genetic and Evolutionary Computation Conference, GECCO\u201917, pages 497\u2013504. ACM, New York, NY, USA, 2017.","DOI":"10.1145\/3071178.3071229"},{"key":"2023033110001594313_j_auto-2019-0116_ref_014_w2aab3b7b5b1b6b1ab1b7c14Aa","unstructured":"Yanan Sun, Bing Xue and Mengjie Zhang. Evolving deep convolutional neural networks for image classification. CoRR, arXiv:1710.10741, 2017."},{"key":"2023033110001594313_j_auto-2019-0116_ref_015_w2aab3b7b5b1b6b1ab1b7c15Aa","unstructured":"Risto Miikkulainen, Jason Liang, Elliot Meyerson, Aditya Rawal, Dan Fink, Olivier Francon, Bala Raju, Hormoz Shahrzad, Arshak Navruzyan, Nigel Duffy and Babak Hodjat. Evolving deep neural networks. arXiv preprint arXiv:1703.00548, 2017."},{"key":"2023033110001594313_j_auto-2019-0116_ref_016_w2aab3b7b5b1b6b1ab1b7c16Aa","doi-asserted-by":"crossref","unstructured":"Kenneth Stanley and Risto Miikkulainen. Evolving neural networks through augmenting topologies. Evolutionary computation, 10(2):99\u2013127, 2002.10.1162\/106365602320169811","DOI":"10.1162\/106365602320169811"},{"key":"2023033110001594313_j_auto-2019-0116_ref_017_w2aab3b7b5b1b6b1ab1b7c17Aa","doi-asserted-by":"crossref","unstructured":"Kenneth\u2009O Stanley, David\u2009B D\u2019Ambrosio and Jason Gauci. A hypercube-based encoding for evolving large-scale neural networks. Artificial life, 15(2):185\u2013212, 2009.10.1162\/artl.2009.15.2.15202","DOI":"10.1162\/artl.2009.15.2.15202"},{"key":"2023033110001594313_j_auto-2019-0116_ref_018_w2aab3b7b5b1b6b1ab1b7c18Aa","doi-asserted-by":"crossref","unstructured":"Aditya Rawal and Risto Miikkulainen. Evolving deep LSTM-based memory networks using an information maximization objective. In Proceedings of the Genetic and Evolutionary Computation Conference 2016, pages 501\u2013508. ACM, 2016.","DOI":"10.1145\/2908812.2908941"},{"key":"2023033110001594313_j_auto-2019-0116_ref_019_w2aab3b7b5b1b6b1ab1b7c19Aa","unstructured":"Aditya Rawal and Risto Miikkulainen. From nodes to networks: Evolving recurrent neural networks. CoRR, arXiv:1803.04439, 2018."},{"key":"2023033110001594313_j_auto-2019-0116_ref_020_w2aab3b7b5b1b6b1ab1b7c20Aa","unstructured":"Andr\u00e9s Camero, Jamal Toutouh and Enrique Alba. Low-cost recurrent neural network expected performance evaluation. arXiv preprint arXiv:1805.07159, 2018."},{"key":"2023033110001594313_j_auto-2019-0116_ref_021_w2aab3b7b5b1b6b1ab1b7c21Aa","unstructured":"Andr\u00e9s Camero, Jamal Toutouh and Enrique Alba. A specialized evolutionary strategy using mean absolute error random sampling to design recurrent neural networks. arXiv preprint arXiv:1909.02425, 2019."},{"key":"2023033110001594313_j_auto-2019-0116_ref_022_w2aab3b7b5b1b6b1ab1b7c22Aa","doi-asserted-by":"crossref","unstructured":"Travis Desell, Sophine Clachar, James Higgins and Brandon Wild. Evolving deep recurrent neural networks using ant colony optimization. In European Conference on Evolutionary Computation in Combinatorial Optimization, pages 86\u201398. Springer, 2015.","DOI":"10.1007\/978-3-319-16468-7_8"},{"key":"2023033110001594313_j_auto-2019-0116_ref_023_w2aab3b7b5b1b6b1ab1b7c23Aa","doi-asserted-by":"crossref","unstructured":"AbdElRahman ElSaid, Fatima El Jamiy, James Higgins, Brandon Wild and Travis Desell. Optimizing long short-term memory recurrent neural networks using ant colony optimization to predict turbine engine vibration. Applied Soft Computing, 2018.","DOI":"10.1145\/3205455.3205637"},{"key":"2023033110001594313_j_auto-2019-0116_ref_024_w2aab3b7b5b1b6b1ab1b7c24Aa","doi-asserted-by":"crossref","unstructured":"Travis\u2009J. Desell, AbdElRahman\u2009A. ElSaid and Alexander\u2009G. Ororbia. An empirical exploration of deep recurrent connections and memory cells using neuro-evolution, 2019.","DOI":"10.1007\/978-981-15-3685-4_10"},{"key":"2023033110001594313_j_auto-2019-0116_ref_025_w2aab3b7b5b1b6b1ab1b7c25Aa","doi-asserted-by":"crossref","unstructured":"AbdElRahman ElSaid, Steven Benson, Shuchita Patwardhan, David Stadem and Desell Travis. Evolving recurrent neural networks for time series data prediction of coal plant parameters. In The 22nd International Conference on the Applications of Evolutionary Computation, Leipzig, Germany, April 2019.","DOI":"10.1007\/978-3-030-16692-2_33"},{"key":"2023033110001594313_j_auto-2019-0116_ref_026_w2aab3b7b5b1b6b1ab1b7c26Aa","doi-asserted-by":"crossref","unstructured":"Alexander Ororbia, AbdElRahman ElSaid and Travis Desell. Investigating recurrent neural network memory structures using neuro-evolution. In Proceedings of the Genetic and Evolutionary Computation Conference, GECCO\u201919, pages 446\u2013455. ACM, New York, NY, USA, 2019.","DOI":"10.1145\/3321707.3321795"},{"key":"2023033110001594313_j_auto-2019-0116_ref_027_w2aab3b7b5b1b6b1ab1b7c27Aa","unstructured":"Shuchita Patwardhan, David Stadem, Matt Fuka and Steve Benson. Condition Based Monitoring and Predicting Ash Behavior in Coal Fired Boilers \u2013 II \u2013 Coal Properties Optimization. Paper Presented at Clearwater Clean Energy Conference, June 2019."},{"key":"2023033110001594313_j_auto-2019-0116_ref_028_w2aab3b7b5b1b6b1ab1b7c28Aa","unstructured":"David Stadem, Shuchita Patwardhan, Matt Fuka, James Langfeld, Alek Benson and Steven Benson. Improving Coal Fired Plant Performance Using a Coal Tracker Optimization Tool. Paper Presented at Pittsburgh Coal Conference, September 2019."}],"container-title":["at - Automatisierungstechnik"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.degruyter.com\/view\/journals\/auto\/68\/2\/article-p130.xml","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/auto-2019-0116\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/auto-2019-0116\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,31]],"date-time":"2023-03-31T10:37:16Z","timestamp":1680259036000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/auto-2019-0116\/html"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1,22]]},"references-count":28,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2020,1,22]]},"published-print":{"date-parts":[[2020,2,25]]}},"alternative-id":["10.1515\/auto-2019-0116"],"URL":"https:\/\/doi.org\/10.1515\/auto-2019-0116","relation":{},"ISSN":["2196-677X","0178-2312"],"issn-type":[{"type":"electronic","value":"2196-677X"},{"type":"print","value":"0178-2312"}],"subject":[],"published":{"date-parts":[[2020,1,22]]}}}