Linear regression models for prediction of annual heating and cooling demand in representative Australian residential dwellings

Aghdaei, Navid Asadzadeh; Kokogiannakis, Georgios; Daly, Daniel; McCarthy, Timothy J.

doi:10.1016/j.egypro.2017.07.482

Cited by 26 publications

(16 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The total heating area of the project is 128 m 2 and the total designed heating load is 5.4 kW. It was calculated with the aid of linear regression model [20] assuming excellent airtightness, levels of ceiling, floor insulation and wall insulation of R 3.5, R 1, and R 2.5 respectively, a single glazing type and window-to-wall ratio equal to 45%. The heat is generated by natural gas boilers, which are to feed the baseline heat demand of the district.…”

Section: Methodsmentioning

confidence: 99%

“…[19], Hyllie's (area under construction in the southern part of Malmö, Sweden) buildings were supposed to have floor and wall heating systems needing a supply temperature of only 30C. Aghdaei et al [20] show an example of simulation designs for various building types and provide a summary of the design order plan for calculating total energy consumption of a dwelling in Australia.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Network Temperatures and Flow Rate: Case Study of District Heating in Canberra, Australia

Чичерин¹

2018

IJEES

View full text Add to dashboard Cite

The purpose of this paper is to highlight the relevance of district heating (DH) in the country where, in general, there is no such system and, specifically, to develop and implement a helpful approach for designing a DH network combining thermal and hydraulic considerations to simulate the energy behaviour of such network. The nonlinear model of the supply and return temperatures describes the dynamics of a DH system with an appropriate accuracy. The results of the generated scenarios are partial load values obtained for each category. The data on the daily heating power demand was transformed into an outdoor temperature dependence curve used to compute the flow rate for each of the scenario. Under the designed condition, the flow is determined and regulation approaches are elaborated. The resulting flow is quite stable. Taking into account the deficiencies of conventional evaluation for DH networks, this study excludes the hypothesis of constant outdoor temperature, and analyzes the influence of outside temperature on the heat losses and electricity consumption for DH networks based upon the state-space method. The obtained results are achieved without significant investments into a DH system just by adjusting and controlling temperatures and flow rates of a heat radiator circulating in the network.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Network Temperatures and Flow Rate: Case Study of District Heating in Canberra, Australia

Чичерин¹

2018

IJEES

View full text Add to dashboard Cite

show abstract

“…The Linear Regression equation is as follows:

Y = b_{0} + b_{1} x_{1} + b_{2} x_{2} + . . \dots + b_{n} x_{n}

where y represents the dependent variable, x 1 , x 2 …………x n are independent variables, b 0 is intercepted and b 1 , b 2 are coefficients and n represent the number of observations. Linear regression models are more accessible and more practical for solving prediction problems (Aghdaei et al, 2017). When there is a single input variable, it is called a simple linear regression, and when there is a multiple‐input variable, it is called a multiple regression model.…”

Section: Machine‐learning Prediction Algorithmsmentioning

confidence: 99%

Pandemic coronavirus disease (Covid‐19): World effects analysis and prediction using machine‐learning techniques

et al. 2021

View full text Add to dashboard Cite

Pandemic novel Coronavirus (Covid‐19) is an infectious disease that primarily spreads by droplets of nose discharge when sneezing and saliva from the mouth when coughing, that had first been reported in Wuhan, China in December 2019. Covid‐19 became a global pandemic, which led to a harmful impact on the world. Many predictive models of Covid‐19 are being proposed by academic researchers around the world to take the foremost decisions and enforce the appropriate control measures. Due to the lack of accurate Covid‐19 records and uncertainty, the standard techniques are being failed to correctly predict the epidemic global effects. To address this issue, we present an Artificial Intelligence (AI)‐based meta‐analysis to predict the trend of epidemic Covid‐19 over the world. The powerful machine learning algorithms namely Naïve Bayes, Support Vector Machine (SVM) and Linear Regression were applied on real time‐series dataset, which holds the global record of confirmed, recovered, deaths and active cases of Covid‐19 outbreak. Statistical analysis has also been conducted to present various facts regarding Covid‐19 observed symptoms, a list of Top‐20 Coronavirus affected countries and a number of coactive cases over the world. Among the three machine learning techniques investigated, Naïve Bayes produced promising results to predict Covid‐19 future trends with less Mean Absolute Error (MAE) and Mean Squared Error (MSE). The less value of MAE and MSE strongly represent the effectiveness of the Naïve Bayes regression technique. Although, the global footprint of this pandemic is still uncertain. This study demonstrates the various trends and future growth of the global pandemic for a proactive response from the citizens and governments of countries. This paper sets the initial benchmark to demonstrate the capability of machine learning for outbreak prediction.

show abstract

“…The procedure of linear regression includes the Dependent variable which is continuous in nature, variable(s) which are autonomous can be continual or discrete, and the straight line formed in this technique. Linear Regression forms a connection between subordinate variable (Y) and to a certain degree one autonomous factor(X) utilizes the line which fits in best manner considered by the term Regression line [1].…”

Section: A) Linear Regressionmentioning

confidence: 99%

A Review on Different Regression Techniques and its applications used in Machine Learning

Kaur

2021

CGCIJCTR

View full text Add to dashboard Cite

Regression Analysis is an assortment of factual methods that fill in as an explanation behind drawing acceptances about associations among the various interrelated elements. This technique is pretty much applicable in almost everywhere in study field which includes biological sciences, social studies and relapse examination. The main purpose of this research paper is to evolve the vital hypothesis for the factual Regression Technique and also to demonstrate the hypothesis with a collection of wide variety of various models looked over economical aspects, demography, sketching and engineering concepts. To assemble the relevant content which is independent from various insights, mathematics based on variable and numerical investigation is included.

show abstract

Linear regression models for prediction of annual heating and cooling demand in representative Australian residential dwellings

Cited by 26 publications

References 14 publications

Network Temperatures and Flow Rate: Case Study of District Heating in Canberra, Australia

Network Temperatures and Flow Rate: Case Study of District Heating in Canberra, Australia

Pandemic coronavirus disease (Covid‐19): World effects analysis and prediction using machine‐learning techniques

A Review on Different Regression Techniques and its applications used in Machine Learning

Contact Info

Product

Resources

About