Machine Learning Decision Support Model for Greenhouse Gas
Reduction Technology Application
I. M. Aliev
Chechen State University, Grozny, Russia
Keywords: Reduction technology, machine learning, GBRT, SVM, DNN, K-ETS.
Abstract: Many countries have implemented policies to reduce greenhouse gas (GHG) emissions since the 21st
Conference of the Parties (COP 21) of the United Nations Framework Convention on Climate Change
(UNFCCC) in 2015. The parties to this convention have voluntarily agreed to a new climate regime that aims
to reduce greenhouse gas emissions. Subsequently, reducing greenhouse gas emissions through specific
reduction technologies (renewable energy) to reduce energy consumption has become a necessity rather than
an option. With the launch of the Korea Emissions Trading Scheme (K-ETS) in 2015, Korea has certified and
funded projects to reduce greenhouse gas emissions. To help the user make informed decisions about the
economic and environmental benefits of using renewable energy, an evaluation model has been developed.
This study establishes a simple assessment method (SAM), an assessment database (DB) of 1199 greenhouse
gas reduction technologies implemented in Korea, and a machine learning-based greenhouse gas reduction
technology assessment model (GRTM). In addition, proposals are made to assess the economic benefits that
can be obtained in combination with the environmental benefits of technology to reduce greenhouse gas
emissions.
1 INTRODUCTION
After the lead version of the Kyoto Protocol of the
United Nations Framework Convention on Climate
Change. In 1997, many countries around the world,
including Korea, made significant efforts to reduce
greenhouse gas (GHG) emissions. In particular,
building energy efficiency has been highlighted both
in Europe and abroad, and many countries are
strengthening building energy efficiency policies by
regulating building design standards, promoting
environmentally friendly benefits, and introducing
zero-energy buildings (Korea Energy Agency, 2018).
In order to successfully reduce spending across the
country, led by the public sector, Korea included an
incentive system for a new climate regime under 100
policy objectives. This required the republican
institution to maintain membership fees (Ministry of
Environment, 2019). As a result, starting in 2020, any
newly constructed building owned by public
institutions is required to introduce energy efficiency
and achieve a 30% reduction in baseline emissions (in
accordance with the agreements of 2007, 2008 and
2009) by 2030 (Ministry of Environment, 2017).
However, lack of experience and understanding
presents a major challenge to the implementation of
this system and is the main reason why the policy is
not effective. Several studies have been carried out to
develop policies to reduce energy consumption or to
analyze the implications of reducing greenhouse gas
emissions from renewable energy. In addition, using
the Emissions Trading Scheme (ETS), a system
designed to support emission reductions, government
agencies voluntarily undertake external emission
reduction projects, certify emission reductions, and
receive economic benefits from emissions trading
(Ministry of Environment, 2018). In previous studies
on the efficiency and control of greenhouse gas
emissions, a greenhouse gas emission prediction
system was developed, which determines the factors
that contribute to emissions fluctuations, with an
emission control system that compares different
buildings based on the developed prediction model. It
is noteworthy that Che (2017) used data analysis
technology to increase the renewable energy market
and compare indicators to select the optimal building
for simulation (Chan, 2017). Chan (2018) predicted
energy production from real-time energy generation
database based on machine learning (Jang, 2018).
Aliev, I.
Machine Learning Decision Support Model for Greenhouse Gas Reduction Technology Application.
DOI: 10.5220/0011556700003524
In Proceedings of the 1st International Conference on Methods, Models, Technologies for Sustainable Development (MMTGE 2022) - Agroclimatic Projects and Carbon Neutrality, pages
165-168
ISBN: 978-989-758-608-8
Copyright
c
2023 by SCITEPRESS Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)
165
2 MATERIALS AND METHODS
A study by nakawa (2018) on reducing energy
consumption and reducing greenhouse gas emissions
analyzed the impact of replacing conventional lamps
with LEDs. Singh (2015) proposed GHG emission
reduction guidelines for GHG emissions forecast up
to 2030 under the ezp program for Israel (Nakano,
2018; Singh, 2015). Further analysis of the efficiency
of renewable energy sources is needed. In addition,
national policy, along with economic and
environmental decision-making regarding renewable
energy sources, should be changed based on the
results of a comprehensive assessment of the energy
performance of buildings. Since the World Economic
Forum in Davos, Switzerland in January 2016, the
idea of a fourth industrial revolution has become a
controversial issue around the world. The fourth
industrial revolution refers to a consumer-oriented
economy based on technological automation and
represents an era when the world is understood
through artificial intelligence (AI) (Bottou). AI refers
to technology that analyzes data through
visualization, machine learning, and data mining to
find better solutions. In AI, machine learning is a
technique that reads huge amounts of data, finds an
algorithm, and predicts changes. In the field of
environmental architecture, there is a growing
number of cases where machine learning is applied to
a system for predicting economic and environmental
benefits. When comparing the predictive ability
between models using traditional statistical technique
and models using machine learning technique,
various studies have shown that the model using
machine learning has a higher level of predictive
ability (Jordan). Therefore, this study proposes an
optimal machine learning model for GHG projects
with a high level of predictive power that uses real
renewable energy sources and examples of external
GHG projects. We believe that this model is superior
to costly and inefficient methods such as energy
modeling. Through this work, we aim to help select
projects with the most significant economic and
environmental benefits compared to the actual energy
consumption of an existing building.
This can be achieved by looking at energy
production in greenhouse gas emission reduction
projects that cover renewable energy, installation
cost, efficiency and energy consumption by use and
source, all of which contribute to emission reductions.
A database of certified GHG emission reductions
from renewable energy sources was created and used
as the basis for building a machine learning model to
support decision-making on GHG emission reduction
projects. To analyze the predictive power of the fitted
model, we used principal component analysis (PCA),
selected gradient boosted regression tree (GBRT),
support vector machine (SVM), and deep neural
network (DNN) machine learning algorithms
optimized for the original data set. in this study. We
also tested the predictive power of machine learning
methods by comparing their predictive power with a
multiple regression model. Unlike previous studies
that only compared the predictive power of different
models, this study examines the applicability of
machine learning to greenhouse gas reduction
technologies by analyzing calculated numerical
values. The Korean government subsidizes a certain
portion of the installation costs to promote the spread
of renewable energy. In response, businesses are
adopting government-subsidized renewable energy
sources such as solar power, solar thermal power, and
geothermal power, along with efficient technologies
including high-efficiency lighting, LED lighting, and
green vehicles (Korea Energy Agency, 2018).
To the extent that reduced energy consumption in
green vehicles can be converted into economic
benefits, this study includes green vehicles in
greenhouse gas emission reduction technologies. We
have defined the introduction of renewable energy
and high-efficiency equipment as "technologies to
reduce greenhouse gas emissions." These
technologies are recognized as emission reduction
certified by the ETS. A GHG technology government
agency converts KoreanOfset Credit (KOC) into
Korean Credit Unit (KCU) and trades at
approximately $19/tCO2e. technology was proposed
to reduce greenhouse gas emissions and a database
was created to convert them into economic value. The
raw data built by the method was used to develop a
model for selecting technologies to reduce
greenhouse gas emissions using machine learning.
3 RESULTS AND DISCUSSION
The methods used to calculate emission reductions
from GHG technology include a direct calculation
using variables and another one using statistical data
(Ministry of Environment, 2017). Based on the
established baseline greenhouse gas abatement
technology unit data, we selected the modeling
methods used for supervised learning in machine
learning and performed the analysis. Although there
are many simulation methods for this type of learning,
GBRT, SVM, and DNN methods were chosen in our
study, which were used to develop predictive models
in previous studies in Korea and abroad. In order to
MMTGE 2022 - I International Conference "Methods, models, technologies for sustainable development: agroclimatic projects and carbon
neutrality", Kadyrov Chechen State University Chechen Republic, Grozny, st. Sher
166
identify the phenomena represented by the collected
data and any problems, we performed exploratory
data analysis (EDA) and data pre-processing. In order
to calculate the certified emission reduction for a
specific GHG emission reduction technology, it is
first necessary to determine the amount of energy
produced or reduced by this technology. In our study,
we proposed a direct assessment of energy production
and reduction in accordance with the application of
greenhouse gas emission reduction technology. The
results were used as input data for the evaluation
database. Solar energy refers to a method of
generating electricity that directly converts solar
energy into electrical energy using the photovoltaic
effect. The annual electricity production from solar
energy can be calculated using the capacity, the
number of installed systems, the electricity utilization
rate and the operation time factors (Peng, 2013).
Solar thermal energy systems harvest solar energy
to heat or preheat water and are often used as the
energy source for water heating. The annual amount
of energy produced by solar thermal collectors can be
calculated by taking into account the installed area of
collectors, the number of households with collectors,
the total reduction from the energy source and the
number of working days. Geothermal energy refers to
the energy of the earth, including hot water and rocks
located deep underground. It is used as an energy
source for cooling and heating buildings. Calculation
of energy production for solar energy, solar thermal
energy and geothermal energy was made by applying
the operating time and electricity utilization factor to
the installed capacity of these sources. Equipment
efficiency among daily storage application rate
calculations was calculated using the statistical
estimates provided in the Greenhouse Gas Emission
Reduction Plan Implementation Guide of the
Ministry of Environment and Renewable Energy of
the National Administrative Urban Construction
Agency. When energy production from GHG
emission reduction technology is not monitored, the
daily storage application factor was calculated taking
into account the load factor, average daily operation
time and geothermal efficiency factor (GFC). When
electricity is used as an energy source for heating and
cooling energy of a building, the geothermal
efficiency factor (GFC) is calculated taking into
account the load factor, the average daily operation
time and the geothermal heating efficiency factor, and
a unit conversion factor of 860 (Mcal/MWh) was
applied (Porfiriev, 2010). The cooling energy GFC
was calculated taking into account the geothermal
heating efficiency factor and the geothermal cooling
efficiency factor. For SFC, the daily amount of solar
heat storage, it was calculated by applying daily solar
radiation, heat collector efficiency and the same unit
conversion factor as GFC.
In order to evaluate high-efficiency equipment in
projects certified by ETS as emission reduction for
government agencies, we separated external
greenhouse gas emission reduction projects that used
high-efficiency equipment, high-efficiency lighting,
LED street lighting installation, and green roofs
(Nikoláeva, 2018). For high-efficiency equipment
such as green rooftops, high-efficiency lighting, LED
street lighting, electric vehicles and natural gas
vehicles, the amount of energy saved was calculated
by comparing the energy consumption before and
after the project. The energy consumption of high-
efficiency lighting and LED street lamps was
calculated using electricity consumption, the number
of luminaires installed and the time the lights were
turned off. Based on the report on the supply and use
of lighting equipment prepared by the Ministry of
Commerce, Industry and Energy and the Korea
Energy Agency, high-efficiency lighting is 5.3 hours,
and the installation of LED street lighting is 10 hours
without light (Porfiriev, 2010). The GHG emission
reductions were calculated by applying the GHG
emission factors for each energy source to the energy
production results and reductions. In order to
establish a standard environmental assessment
database for greenhouse gas emission reduction
projects, in our study, the amount of energy produced
and reduced by greenhouse gas emission reduction
technology was converted to greenhouse gas
emission reduction when developing the assessment
database with greenhouse gas emissions as base unit.
4 CONCLUSIONS
After studying 1199 technology projects to reduce
greenhouse gas emissions, a method was proposed for
estimating the amount of energy reduced and
produced, and a certified greenhouse gas emission
reduction (KOC) method was proposed. Using 1199
GHG technology projects, SAM was created, which
is a GHG technology assessment database. To
consider energy consumption patterns and
environmental conditions in a building, SAM was
created to evaluate the effect of reducing greenhouse
gas emissions across different uses and energy
sources. These include heating, cooling, lighting,
ventilation and water heating, as well as energy
sources such as electricity, city gas and heat. Based
on the original SAM data, machine learning methods
(GBRT, SVM and DNN) were used to develop
Machine Learning Decision Support Model for Greenhouse Gas Reduction Technology Application
167
GRTM, a model that supports decision making for
greenhouse gas reduction technologies.
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neutrality", Kadyrov Chechen State University Chechen Republic, Grozny, st. Sher
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