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.

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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. THANK YOU FOR YOUR
ATTENTION
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CONTACT
E-Mail: giulio@giuliovialetto.it
Site: www.giuliovialetto.it