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An innovative approach to design cogeneration systems based on big data analysis and use of clustering methods

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In recent years, collecting energy consumption data is becoming easier and easier thanks to decreasing of cost of smart sensors. Moreover, capacity of analysis data using big data methods like machine learning and artificial intelligence is increasing. Such methods are expected to be useful to increase efficiency of energy systems.
In this paper an innovative approach to design cogeneration systems based on big data analysis is developed. More specifically, a study on how cluster analysis could be applied to analyse energy consumption data is depicted. The aim of the method is to design cogeneration systems that suit more efficiently energy demand profiles, choosing the correct type of cogeneration technology, operation strategy and, if they are necessary, energy storages. In the first part of the paper, the methodology based on clustering to perform the analysis of the dataset is described. In the second part, a case study with cogenerators (a wood industry that requires low temperature heat to dry wood into steam-powered kilns) is analysed. An alternative cogeneration system is designed and proposed. Thermodynamics benchmarks are defined to evaluate differences between as-is and alternative scenarios.
Results show that the proposed innovative method allows to choose a more suitable cogeneration technology compared to the adopted one, giving suggestions on the operation strategy in order to decrease energy losses and, consequently, primary energy consumption.

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An innovative approach to design cogeneration systems based on big data analysis and use of clustering methods

  1. 1. AN INNOVATIVE APPROACH TO DESIGN COGENERATION SYSTEMS BASED ON BIG DATAANALYSIS AND USE OF CLUSTERING METHODS aDr. Giulio Vialetto, bProf. Marco Noro aDepartment of Industrial Engineering bDepartment of Management and Engineering University of Padova (Italy)
  2. 2. An innovative approach to design cogeneration systems based on big data analysis and use of clustering methods (Dr. G. Vialetto, Prof. M. Noro) OVERVIEW METHODOLOGY CASE STUDY CONCLUSION In the recent years collecting, storing and processing data is becoming cheaper thanks to the improvements on sensors, network and CPU. Internet of Things (IoT) is proposed to connect sensors to network or internet.
  3. 3. An innovative approach to design cogeneration systems based on big data analysis and use of clustering methods (Dr. G. Vialetto, Prof. M. Noro) OVERVIEW METHODOLOGY CASE STUDY CONCLUSION Meanwhile more data on energy demands are available, energy system are still analysed using cumulative curve of consumption. In a case that two types of energy (for example heat and electricity) are consumed, it is unknown which correlations there are between them. (Figure taken from A. Biglia, F. V. Caredda, E. Fabrizio, M. Filippi, and N. Mandas, “Technical-economic feasibility of CHP systems in large hospitals through the Energy Hub method: The case of Cagliari AOB,” Energy Build., vol. 147, pp. 101–112, Jul. 2017)
  4. 4. OVERVIEW METHODOLOGY CASE STUDY CONCLUSION An innovative approach to design cogeneration systems based on big data analysis and use of clustering methods (Dr. G. Vialetto, Prof. M. Noro) It is proposed to use cluster analysis to perform clustering on energy data demands. The main scope is to divide the observed data into homogenous groups and use them to design and size an energy system.
  5. 5. OVERVIEW METHODOLOGY CASE STUDY CONCLUSION An innovative approach to design cogeneration systems based on big data analysis and use of clustering methods (Dr. G. Vialetto, Prof. M. Noro) Two different analyses based on clustering are proposed: • Power analysis, every observation is considered separately to define clusters with similar values of the variables (i.e. electricity demand and H/P ratio). This information, and how such variables vary inside the cluster, will suggest the most suitable polygeneration technology and/or information to design the generation system; • Profile analysis, daily energy demand profile (not a single observation) is defined and clustered to identify how energy demand varies during daytime. Possible mismatching can be detected between energy demand and energy production using energy system defined with Power analysis.
  6. 6. OVERVIEW METHODOLOGY CASE STUDY CONCLUSION An innovative approach to design cogeneration systems based on big data analysis and use of clustering methods (Dr. G. Vialetto, Prof. M. Noro) A workflow is then proposed to perform cluster analysis both for power and profile analysis. Data cleaning is necessary to clean dataset from missing and/or bad measurement records. DATA CLEANING DATASET CREATION EST. OF NUMBER OF CLUSTER CLUSTER ANALYSIS
  7. 7. OVERVIEW METHODOLOGY CASE STUDY CONCLUSION An innovative approach to design cogeneration systems based on big data analysis and use of clustering methods (Dr. G. Vialetto, Prof. M. Noro) Power analysis clusters data into homogeneous groups. Average curves based on observation data can be used to define which is the most suitable component for the generation system.
  8. 8. OVERVIEW METHODOLOGY CASE STUDY CONCLUSION An innovative approach to design cogeneration systems based on big data analysis and use of clustering methods (Dr. G. Vialetto, Prof. M. Noro) Profile analysis defines daily observation as datum to divide into homogenous groups similar patterns of demand. An average curve of consumption is then defined to check mismatching between energy demand and production, to define energy storage and operation strategy.
  9. 9. OVERVIEW METHODOLOGY CASE STUDY CONCLUSION An innovative approach to design cogeneration systems based on big data analysis and use of clustering methods (Dr. G. Vialetto, Prof. M. Noro) A case study is proposed concerning an industrial facility selling wood (timber) window laminated, plywood, engineered veneer, laminate, flooring and white wood. The industrial process requires to dry wood into kilns, and to store it into warehouses. Electricity is used for the production equipment, offices, lighting purpose into the warehouses, and to charge electric forklifts. Heat is used to produce steam for the kilns that work at about 70 °C.
  10. 10. OVERVIEW METHODOLOGY CASE STUDY CONCLUSION An innovative approach to design cogeneration systems based on big data analysis and use of clustering methods (Dr. G. Vialetto, Prof. M. Noro) POWER ANALYSIS
  11. 11. OVERVIEW METHODOLOGY CASE STUDY CONCLUSION An innovative approach to design cogeneration systems based on big data analysis and use of clustering methods (Dr. G. Vialetto, Prof. M. Noro) Cluster Number of observations 1 31.91 % 2 21.90 % 3 0.27 % 4 45.92 % PROFILE ANALYSIS
  12. 12. OVERVIEW METHODOLOGY CASE STUDY CONCLUSION An innovative approach to design cogeneration systems based on big data analysis and use of clustering methods (Dr. G. Vialetto, Prof. M. Noro) On the dataset both power and profile analyses are performed. Firstly power analysis suggests the most suitable cogeneration system – micro gas turbines. Profile analysis gives also useful information to define operation strategy and energy storage (in this case heat).
  13. 13. OVERVIEW METHODOLOGY CASE STUDY CONCLUSION An innovative approach to design cogeneration systems based on big data analysis and use of clustering methods (Dr. G. Vialetto, Prof. M. Noro) Two different TO BE scenarios are proposed to improve efficiency on energy generation. First, an improvement only on energy generation (microturbines) is proposed with heat storage.
  14. 14. OVERVIEW METHODOLOGY CASE STUDY CONCLUSION An innovative approach to design cogeneration systems based on big data analysis and use of clustering methods (Dr. G. Vialetto, Prof. M. Noro) In scenario TO BE 2 operation strategy is improved, cogeneration stops when heat storage is not able to store more heat: the aim is to avoid heat losses.
  15. 15. OVERVIEW METHODOLOGY CASE STUDY CONCLUSION An innovative approach to design cogeneration systems based on big data analysis and use of clustering methods (Dr. G. Vialetto, Prof. M. Noro) Analysis on primary energy saving (PES) between AS IS and TO BE scenarios is then performed. It is possible to appreciate that saving of 6 % can be achieved. Heat storage is important to achieve this goal: the mean heat stored level is close to 50 % covering between 4 - 5 % on total heat demand (IC). Scenario Primary energy Saving AS IS 6.505 GWh - TO BE 1 6.377 GWh 2.01 % TO BE 2 6.137 GWh 6.00 % 𝑃𝐸 = 𝐹 + 𝐸𝑔𝑟𝑖𝑑,𝑖𝑛 − 𝐸𝑔𝑟𝑖𝑑,𝑜𝑢𝑡 0.434 𝐼𝑆 = 𝐻𝑠𝑡𝑜𝑟𝑒𝑑,𝑖𝑛 𝐻 𝐶𝐻𝑃 𝐼 𝐶 = 𝐻𝑠𝑡𝑜𝑟𝑒𝑑,𝑜𝑢𝑡 𝐻 𝑢𝑠𝑒𝑟 Scenario IS IC % Mean heat stored TO BE 1 4.6 % 4.3 % 50.5 % TO BE 2 5.7 % 4.7 % 48.9 %
  16. 16. OVERVIEW METHODOLOGY CASE STUDY CONCLUSION An innovative approach to design cogeneration systems based on big data analysis and use of clustering methods (Dr. G. Vialetto, Prof. M. Noro) • An innovative method to design energy system based on clustering here is proposed. • Energy demand data are divided into homogenous groups: ▪ to define which is the most suitable energy generation technology with power analysis; ▪ profile analysis is then used to check if energy storage occurs and/or which is the most suitable operation strategy. • Proposed methodology is then applied to an industrial case study to enhance its energy cogeneration system. • It was demonstrated that a PES of 6 % can be achieve improving energy generation.
  17. 17. THANK YOU FOR YOUR ATTENTION ANY QUESTION? CONTACT E-Mail: giulio@giuliovialetto.it Site: www.giuliovialetto.it

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