Dear Mr.Muhammad ;

Through the optimization of the model of a universal residential building with minimum energyparameters as passive energy efficiency measures, a multi-objective optimization will be performed in order to find the best solution that will allow a compromise between the building life cycle cost, energy savings, and thermal comfort. Building orientation, window type, Window-to-Wall Ratio, wall and roof insulation, and infiltration rate are among the design elements considered.

Dear Muhammad,

The straightforward answer is yes.

You would need to define the level of refinement for your model.

Is this PhD level and how long do you have to complete the initial phase?

Back in 2003 I remember following the ASHRE guidelines to answer a similar question to yours.

there are several considerations (A few below):

• 1. local climatic conditions (heating and cooling).

a. You could go for degree days at a higher level or if you have access to meteorological data then three hourly or hourly data will give better heat flows. Ten-minute data I have found can create huge sets that quickly become unwieldy in Excel so alternative solutions are required (my path was Excel, VBA then FORTRAN, purely because of the amount of raw data). You will need to decide to what level you wish to go.

b. Sun Path throughout the year and angle of incidence (a local pyranometer would be excellent here)

c. Level of atmospheric pollution

d. Obstructions such as buildings and geographical features.

• 2. Building construction:

a. Materials

b. Thermal pathways (bonds/breaks/insulation)

c. Thermal masses (storage)

d. Solar shading / ingress

e. Ventilation (air changes per hour) passive or active heating and cooling

f. Orientation

• 3. Occupation and usage, sensible heat sources
• 4. The straight forward method is to apply the laws of thermodynamics to the problem. For each element (wall, window, roof etc.) define the type of heat transfer (Radiation, Conductive or Convective). The energy balance is then a case of adding and subtracting gains and losses, internally and externally.
• 5. Then main caveat is that this is all predictive and uses historic data for modelling. One could apply the Intergovernmental Panel on Climate Change Assessment Report for different scenarios. These tend to ne macro climate rather than micro climate based so may not have the accuracy for your local situation.

Pulling all the equations together, although straightforward can take a bit of time Full time and knowing the subject I would estimate the first working model would take in the region of two weeks (80 hours). Depending upon the level of refinement required one could easily put another week or two on top of that. Checking all the formulae and code (modules) are calculating correctly is where time is burnt.

There may possibly be parts of what you are looking for already written in Python to use as an example to guide how others have approached this issue, be careful not to plagiarise someone else's code if you do this.

This is a high level take on your question, hopefully helpful.

Regards

Andrew Robson

Dear Jarral,

please see

https://www.amazon.com/dp/1636485359/

best wishes

Dear Almasri,

may please share free sources link

thanks