IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v181y2022icp142-155.html
   My bibliography  Save this article

Waste-to-energy forecasting and real-time optimization: An anomaly-aware approach

Author

Listed:
  • Teng, Sin Yong
  • Máša, Vítězslav
  • Touš, Michal
  • Vondra, Marek
  • Lam, Hon Loong
  • Stehlík, Petr

Abstract

Waste-to-energy (WTE) technologies convert municipal solid, and biomass wastes into affordable renewable heat and power energy. However, there are large uncertainties associated with using waste feed as a renewable energy source. This paper proposes a WTE management tool that provides forecasting and real-time optimization of power generated with the consideration of anomaly. The WTE management framework was designed based on a biological neural network, the Hierarchical Temporal Memory (HTM) coupled with a dual-mode optimization procedure. The HTM model is inspired by the mechanism in the cerebral neocortex of the brain, providing anomaly identification and spatial-temporal prediction. In this work, the HTM-based smart energy framework is demonstrated in an industrial case study for the power generation of a waste-to-energy cogeneration system. HTM was compared with methods such as Long Short-Term Memory (LSTM) neural network, Autoregressive Integrated Moving Average (ARIMA), Fourier Transformation Extrapolation (FTE), persistence forecasting, and was able to achieve mean squared error (MSE) of 0.08466% while giving 35450 Euro profit in half a year. Coupled with a novel dual-mode optimization procedure, HTM demonstrated 11% improvement with respect to only predictive optimization (with HTM) in estimated gross profit.

Suggested Citation

  • Teng, Sin Yong & Máša, Vítězslav & Touš, Michal & Vondra, Marek & Lam, Hon Loong & Stehlík, Petr, 2022. "Waste-to-energy forecasting and real-time optimization: An anomaly-aware approach," Renewable Energy, Elsevier, vol. 181(C), pages 142-155.
  • Handle: RePEc:eee:renene:v:181:y:2022:i:c:p:142-155
    DOI: 10.1016/j.renene.2021.09.026
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148121013252
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.renene.2021.09.026?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Chae, Song Hwa & Kim, Sang Hun & Yoon, Sung-Geun & Park, Sunwon, 2010. "Optimization of a waste heat utilization network in an eco-industrial park," Applied Energy, Elsevier, vol. 87(6), pages 1978-1988, June.
    2. Sun, Mucun & Feng, Cong & Zhang, Jie, 2019. "Conditional aggregated probabilistic wind power forecasting based on spatio-temporal correlation," Applied Energy, Elsevier, vol. 256(C).
    3. Yu, Mengmeng & Hong, Seung Ho, 2017. "Incentive-based demand response considering hierarchical electricity market: A Stackelberg game approach," Applied Energy, Elsevier, vol. 203(C), pages 267-279.
    4. Wang, Yi & Gan, Dahua & Sun, Mingyang & Zhang, Ning & Lu, Zongxiang & Kang, Chongqing, 2019. "Probabilistic individual load forecasting using pinball loss guided LSTM," Applied Energy, Elsevier, vol. 235(C), pages 10-20.
    5. Malinauskaite, J. & Jouhara, H. & Czajczyńska, D. & Stanchev, P. & Katsou, E. & Rostkowski, P. & Thorne, R.J. & Colón, J. & Ponsá, S. & Al-Mansour, F. & Anguilano, L. & Krzyżyńska, R. & López, I.C. & , 2017. "Municipal solid waste management and waste-to-energy in the context of a circular economy and energy recycling in Europe," Energy, Elsevier, vol. 141(C), pages 2013-2044.
    6. He, Yaoyao & Qin, Yang & Wang, Shuo & Wang, Xu & Wang, Chao, 2019. "Electricity consumption probability density forecasting method based on LASSO-Quantile Regression Neural Network," Applied Energy, Elsevier, vol. 233, pages 565-575.
    7. Namuli, R. & Jaumard, B. & Awasthi, A. & Pillay, P., 2013. "Optimisation of biomass waste to energy conversion systems for rural grid-connected applications," Applied Energy, Elsevier, vol. 102(C), pages 1013-1021.
    8. Ngan, Sue Lin & How, Bing Shen & Teng, Sin Yong & Promentilla, Michael Angelo B. & Yatim, Puan & Er, Ah Choy & Lam, Hon Loong, 2019. "Prioritization of sustainability indicators for promoting the circular economy: The case of developing countries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 111(C), pages 314-331.
    9. Liu, Yongqi & Qin, Hui & Zhang, Zhendong & Pei, Shaoqian & Wang, Chao & Yu, Xiang & Jiang, Zhiqiang & Zhou, Jianzhong, 2019. "Ensemble spatiotemporal forecasting of solar irradiation using variational Bayesian convolutional gate recurrent unit network," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    10. Xin-gang, Zhao & Gui-wu, Jiang & Ang, Li & Yun, Li, 2016. "Technology, cost, a performance of waste-to-energy incineration industry in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 115-130.
    11. Ngan, Sue Lin & How, Bing Shen & Teng, Sin Yong & Promentilla, Michael Angelo B. & Yatim, Puan & Er, Ah Choy & Lam, Hon Loong, 2019. "Prioritization of sustainability indicators for promoting the circular economy: The case of developing countries," MPRA Paper 95450, University Library of Munich, Germany, revised 01 Jun 2019.
    12. Touš, Michal & Pavlas, Martin & Putna, Ondřej & Stehlík, Petr & Crha, Lukáš, 2015. "Combined heat and power production planning in a waste-to-energy plant on a short-term basis," Energy, Elsevier, vol. 90(P1), pages 137-147.
    13. Yuan, Chaoqing & Liu, Sifeng & Fang, Zhigeng, 2016. "Comparison of China's primary energy consumption forecasting by using ARIMA (the autoregressive integrated moving average) model and GM(1,1) model," Energy, Elsevier, vol. 100(C), pages 384-390.
    14. Oh, Eunsung & Son, Sung-Yong, 2018. "Energy-storage system sizing and operation strategies based on discrete Fourier transform for reliable wind-power generation," Renewable Energy, Elsevier, vol. 116(PA), pages 786-794.
    15. Suganthi, L. & Samuel, Anand A., 2012. "Energy models for demand forecasting—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(2), pages 1223-1240.
    16. Xenos, Dionysios P. & Cicciotti, Matteo & Kopanos, Georgios M. & Bouaswaig, Ala E.F. & Kahrs, Olaf & Martinez-Botas, Ricardo & Thornhill, Nina F., 2015. "Optimization of a network of compressors in parallel: Real Time Optimization (RTO) of compressors in chemical plants – An industrial case study," Applied Energy, Elsevier, vol. 144(C), pages 51-63.
    17. Yan Bao & Yu Luo & Weige Zhang & Mei Huang & Le Yi Wang & Jiuchun Jiang, 2018. "A Bi-Level Optimization Approach to Charging Load Regulation of Electric Vehicle Fast Charging Stations Based on a Battery Energy Storage System," Energies, MDPI, vol. 11(1), pages 1-21, January.
    18. Sen, Parag & Roy, Mousumi & Pal, Parimal, 2016. "Application of ARIMA for forecasting energy consumption and GHG emission: A case study of an Indian pig iron manufacturing organization," Energy, Elsevier, vol. 116(P1), pages 1031-1038.
    19. Máša, Vítězslav & Stehlík, Petr & Touš, Michal & Vondra, Marek, 2018. "Key pillars of successful energy saving projects in small and medium industrial enterprises," Energy, Elsevier, vol. 158(C), pages 293-304.
    20. Shuming Wang & Tsan Sheng Ng & Manyu Wong, 2016. "Expansion planning for waste‐to‐energy systems using waste forecast prediction sets," Naval Research Logistics (NRL), John Wiley & Sons, vol. 63(1), pages 47-70, February.
    21. Kuznetsova, Elizaveta & Cardin, Michel-Alexandre & Diao, Mingzhen & Zhang, Sizhe, 2019. "Integrated decision-support methodology for combined centralized-decentralized waste-to-energy management systems design," Renewable and Sustainable Energy Reviews, Elsevier, vol. 103(C), pages 477-500.
    22. Putna, Ondřej & Janošťák, František & Šomplák, Radovan & Pavlas, Martin, 2018. "Demand modelling in district heating systems within the conceptual design of a waste-to-energy plant," Energy, Elsevier, vol. 163(C), pages 1125-1139.
    23. Abdel-Aal, R.E. & Elhadidy, M.A. & Shaahid, S.M., 2009. "Modeling and forecasting the mean hourly wind speed time series using GMDH-based abductive networks," Renewable Energy, Elsevier, vol. 34(7), pages 1686-1699.
    24. Lan, Hai & Zhang, Chi & Hong, Ying-Yi & He, Yin & Wen, Shuli, 2019. "Day-ahead spatiotemporal solar irradiation forecasting using frequency-based hybrid principal component analysis and neural network," Applied Energy, Elsevier, vol. 247(C), pages 389-402.
    25. Qing, Xiangyun & Niu, Yugang, 2018. "Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM," Energy, Elsevier, vol. 148(C), pages 461-468.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Guo‐Feng Fan & Yan‐Hui Guo & Jia‐Mei Zheng & Wei‐Chiang Hong, 2020. "A generalized regression model based on hybrid empirical mode decomposition and support vector regression with back‐propagation neural network for mid‐short‐term load forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(5), pages 737-756, August.
    2. Wang, Qiang & Li, Shuyu & Li, Rongrong, 2018. "Forecasting energy demand in China and India: Using single-linear, hybrid-linear, and non-linear time series forecast techniques," Energy, Elsevier, vol. 161(C), pages 821-831.
    3. Przemysław Zaleski & Yash Chawla, 2020. "Circular Economy in Poland: Profitability Analysis for Two Methods of Waste Processing in Small Municipalities," Energies, MDPI, vol. 13(19), pages 1-26, October.
    4. Yu Hao & Yingting Wang & Qiuwei Wu & Shiwei Sun & Weilu Wang & Menglin Cui, 2020. "What affects residents' participation in the circular economy for sustainable development? Evidence from China," Sustainable Development, John Wiley & Sons, Ltd., vol. 28(5), pages 1251-1268, September.
    5. Kong, Xiangfei & Du, Xinyu & Xue, Guixiang & Xu, Zhijie, 2023. "Multi-step short-term solar radiation prediction based on empirical mode decomposition and gated recurrent unit optimized via an attention mechanism," Energy, Elsevier, vol. 282(C).
    6. Yeo, Lip Siang & Teng, Sin Yong & Ng, Wendy Pei Qin & Lim, Chun Hsion & Leong, Wei Dong & Lam, Hon Loong & Wong, Yat Choy & Sunarso, Jaka & How, Bing Shen, 2022. "Sequential optimization of process and supply chains considering re-refineries for oil and gas circularity," Applied Energy, Elsevier, vol. 322(C).
    7. Syed Aziz Ur Rehman & Yanpeng Cai & Rizwan Fazal & Gordhan Das Walasai & Nayyar Hussain Mirjat, 2017. "An Integrated Modeling Approach for Forecasting Long-Term Energy Demand in Pakistan," Energies, MDPI, vol. 10(11), pages 1-23, November.
    8. Wen, Lulu & Zhou, Kaile & Li, Jun & Wang, Shanyong, 2020. "Modified deep learning and reinforcement learning for an incentive-based demand response model," Energy, Elsevier, vol. 205(C).
    9. Wang, Qiang & Jiang, Feng, 2019. "Integrating linear and nonlinear forecasting techniques based on grey theory and artificial intelligence to forecast shale gas monthly production in Pennsylvania and Texas of the United States," Energy, Elsevier, vol. 178(C), pages 781-803.
    10. Konuralp Pamukcu, 2020. "Implementation of Sustainable Development Goals Through the Equimarginal Principle and Circular Economy," Istanbul Journal of Economics-Istanbul Iktisat Dergisi, Istanbul University, Faculty of Economics, vol. 70(2), pages 267-286, December.
    11. Jason Yi Juang Yeo & Bing Shen How & Sin Yong Teng & Wei Dong Leong & Wendy Pei Qin Ng & Chun Hsion Lim & Sue Lin Ngan & Jaka Sunarso & Hon Loong Lam, 2020. "Synthesis of Sustainable Circular Economy in Palm Oil Industry Using Graph-Theoretic Method," Sustainability, MDPI, vol. 12(19), pages 1-29, September.
    12. Jacob Hale & Suzanna Long, 2020. "A Time Series Sustainability Assessment of a Partial Energy Portfolio Transition," Energies, MDPI, vol. 14(1), pages 1-14, December.
    13. Jawaher Binsuwadan & Ghadda Yousif & Hiyam Abdulrahim & Hind Alofaysan, 2023. "The Role of the Circular Economy in Fostering Sustainable Economic Growth in the GCC," Sustainability, MDPI, vol. 15(22), pages 1-17, November.
    14. Hu, Yusha & Man, Yi, 2023. "Energy consumption and carbon emissions forecasting for industrial processes: Status, challenges and perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 182(C).
    15. Hu, Huanling & Wang, Lin & Lv, Sheng-Xiang, 2020. "Forecasting energy consumption and wind power generation using deep echo state network," Renewable Energy, Elsevier, vol. 154(C), pages 598-613.
    16. Mahdi Asadi & Iman Larki & Mohammad Mahdi Forootan & Rouhollah Ahmadi & Meisam Farajollahi, 2023. "Long-Term Scenario Analysis of Electricity Supply and Demand in Iran: Time Series Analysis, Renewable Electricity Development, Energy Efficiency and Conservation," Sustainability, MDPI, vol. 15(5), pages 1-24, March.
    17. Hrabec, Dušan & Šomplák, Radovan & Nevrlý, Vlastimír & Viktorin, Adam & Pluháček, Michal & Popela, Pavel, 2020. "Sustainable waste-to-energy facility location: Influence of demand on energy sales," Energy, Elsevier, vol. 207(C).
    18. Peng, Tian & Zhang, Chu & Zhou, Jianzhong & Nazir, Muhammad Shahzad, 2021. "An integrated framework of Bi-directional long-short term memory (BiLSTM) based on sine cosine algorithm for hourly solar radiation forecasting," Energy, Elsevier, vol. 221(C).
    19. Zheng, Jianqin & Zhang, Haoran & Dai, Yuanhao & Wang, Bohong & Zheng, Taicheng & Liao, Qi & Liang, Yongtu & Zhang, Fengwei & Song, Xuan, 2020. "Time series prediction for output of multi-region solar power plants," Applied Energy, Elsevier, vol. 257(C).
    20. Shuyu Li & Xuan Yang & Rongrong Li, 2019. "Forecasting Coal Consumption in India by 2030: Using Linear Modified Linear (MGM-ARIMA) and Linear Modified Nonlinear (BP-ARIMA) Combined Models," Sustainability, MDPI, vol. 11(3), pages 1-19, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:renene:v:181:y:2022:i:c:p:142-155. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/renewable-energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.