scholarly journals Analysis of methods and techniques for prediction of natural gas consumption

2019 ◽  
Vol 43 (1) ◽  
pp. 99-117 ◽  
Author(s):  
Dario Šebalj ◽  
Josip Mesarić ◽  
Davor Dujak
Keyword(s):  
Natural Gas ◽  
Prediction Models ◽  
Weather Data ◽  
Support Vector ◽  
Systematic Analysis ◽  
Scientific Databases ◽  
Gas Consumption ◽  
Natural Gas Consumption ◽  
Input Variables ◽  
Overview Analysis

Due to its many advantages, demand for natural gas has increased considerably and many models for predicting natural gas consumption are developed. The aim of this paper is to present an overview and systematic analysis of the latest research papers that deal with predictions of natural gas consumption for residential and commercial use from the year 2002 to 2017. Literature overview analysis was conducted using the two most relevant scientific databases Web of Science Core Collection and Scopus. The results indicate neural networks as the most common method used for predictions of natural gas consumption, while most accurate methods are genetic algorithms, support vector machines and ANFIS. Most used input variables are past natural gas consumption data and weather data, and prediction is most commonly made on daily and annual level on a country area level. Limitations of the research raise from relatively small number of analyzed papers but still research could be used for significant improving of prediction models for natural gas consumption.

A Methodology to Quantify Residential Energy-Efficiency in a Heating-Dominated Climate

2011 ◽  
Author(s):  
Sean Casey ◽  
Marcus Bianchi ◽  
David Roberts ◽  
Moncef Krarti
Keyword(s):  
Energy Efficiency ◽  
Natural Gas ◽  
Inverse Modeling ◽  
Weather Data ◽  
Mechanical Equipment ◽  
Energy Assessment ◽  
Consumption Data ◽  
Gas Consumption ◽  
Natural Gas Consumption ◽  
The Difference

A methodology is presented that uses readily available information such as energy consumption data, limited building characteristics, and local daily temperature data to identify energy-inefficient homes in a heating-dominated climate. Specifically, this methodology is applied to 327 owner-occupied, single-family homes in Boulder, Colorado, which are compared to simulated prototype homes. A home’s energy-efficiency is characterized by its construction properties, such as insulation R-values, infiltration rates, and mechanical equipment efficiencies. Previous research indicates a close relationship between these properties and inverse modeling parameters, such as the heating slope (HS) values from variable-base degree-day (VBDD) models. The methodology compares the HS values from VBDD models of monthly natural gas consumption data to simulated HS values of reference homes. The difference, ΔHS, is the primary criterion for quantifying a home’s energy-efficiency and energy retrofit potential. To validate the results of the methodology, the results from a detailed energy assessment of a field-test home are used. Using the natural gas consumption noted in the utility data and historical weather data for the dates of bill, a VBDD model is created and the HSfield-test is calculated. HSreference of a 2009-IECC reference home of identical size is calculated and the difference, ΔHS, is calculated. Using UA-values and mechanical efficiencies from the energy assessment report, the theoretical HS values are calculated for both the assessed home and the reference home. The difference, ΔHStheoretical, is calculated. Overall, a 24% difference is found between the ΔHS and ΔHStheoretical. While the accuracy can be improved, the implication is that the energy-efficiency of homes can be inferred from inverse modeling of utility data under a specific set of conditions.

Short-Term Forecasting of Natural Gas Consumption Using Factor Selection Algorithm and Optimized Support Vector Regression

2018 ◽  
Vol 141 (3) ◽  
Author(s):  
Nan Wei ◽  
Changjun Li ◽  
Chan Li ◽  
Hanyu Xie ◽  
Zhongwei Du ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Natural Gas ◽  
Support Vector Regression ◽  
Series Data ◽  
Support Vector ◽  
Selection Algorithm ◽  
Short Term ◽  
Proposed Model ◽  
Gas Consumption ◽  
Natural Gas Consumption

Forecasting of natural gas consumption has been essential for natural gas companies, customers, and governments. However, accurate forecasting of natural gas consumption is difficult, due to the cyclical change of the consumption and the complexity of the factors that influence the consumption. In this work, we constructed a hybrid artificial intelligence (AI) model to predict the short-term natural gas consumption and examine the effects of the factors in the consumption cycle. The proposed model combines factor selection algorithm (FSA), life genetic algorithm (LGA), and support vector regression (SVR), namely, as FSA-LGA-SVR. FSA is used to select factors automatically for different period based on correlation analysis. The LGA optimized SVR is utilized to provide the prediction of time series data. To avoid being trapped in local minima, the hyper-parameters of SVR are determined by LGA, which is enhanced due to newly added “learning” and “death” operations in conventional genetic algorithm. Additionally, in order to examine the effects of the factors in different period, we utilized the recent data of three big cities in Greece and divided the data into 12 subseries. The prediction results demonstrated that the proposed model can give a better performance of short-term natural gas consumption forecasting compared to the estimation value of existing models. Particularly, the mean absolute range normalized errors of the proposed model in Athens, Thessaloniki, and Larisa are 1.90%, 2.26%, and 2.12%, respectively.

EXPERIENCE, PROSPECTS AND PROBLEMS OF COOPERATION BETWEEN CHINA AND TURKMENISTAN IN THE FIELDOF NATURAL GAS

2019 ◽  
pp. 323-329
Author(s):  
Y. JIA
Keyword(s):  
Natural Gas ◽  
Central Asia ◽  
Oil And Gas ◽  
The European Union ◽  
Multilateral Cooperation ◽  
Industrial Chain ◽  
The Usa ◽  
Gas Consumption ◽  
Slow Progress ◽  
Natural Gas Consumption

Since 2007, the use of natural gas in China depends on the import, and with an increase in natural gas consumption, gas imports are also constantly growing. In 2018, Chinas natural gas imports approached 100 billion cubic meters, which is 70 times more than in 2006. In recent years, increasing attention has been paid to the use of natural gas in China. Turkmenistan is Chinas main source of pipeline gas imports, and China is Turkmenistans largest exporter of natural gas. In the framework of the traditional model of oil and gas cooperation, China and Turkmenistan are facing such problems as the uniform content of cooperation, lack of close ties in the field of multilateral cooperation and slow progress in the development of the entire industrial chain. Cooperation between China and Central Asia in the field of oil and gas is increasingly affecting the nerves of other countries, except the five countries of Central Asia, but including Russia, Afghanistan, Pakistan, India, Iran and other countries of the Middle East, Japan, South Korea, etc. and even the European Union and the USA. Despite the favorable trading environment for both parties, there are also problems in the domestic market of Turkmenistan and the risks of international competition.

Forecasting the daily natural gas consumption with an accurate white-box model

Energy ◽  
2021 ◽  
pp. 121036
Author(s):  
Nan Wei ◽  
Lihua Yin ◽  
Chao Li ◽  
Changjun Li ◽  
Christine Chan ◽  
...  
Keyword(s):  
Natural Gas ◽  
Box Model ◽  
Gas Consumption ◽  
Natural Gas Consumption
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