This complex systems model depicts the impact of factors such as violence and community programs on the youth of Bourke. The time scale is in months and shows the next 6 years. The model aims to show how by altering expenditure in different areas, the town of Bourke can decrease crime and increase their population involvement in community programs. This model is intended to be dynamic to allow the user to change certain variables to see changes in impact

Units Used in the Model:

• Electricity Supply: Measured in MWh/h (megawatt-hours per hour), representing the total amount of electricity available from both external sources and local solar generation.
• Electricity Demand: Measured in MWh/h, covering both household and business consumption.
• Micro-Generated Solar Electricity: Solar power generated by households and businesses, measured in MWh/h.

Key Components:

1. Electricity Supply: The total electricity provided by both external sources (the grid) and solar generation.
2. Electricity Demand: The total community demand, which includes household and business consumption and varies over a 24-hour period based on usage patterns.
3. Micro-Generated Solar: Solar power generated by both households and businesses. This power reduces the demand for grid electricity and follows a sinusoidal pattern, peaking during the day and dropping to zero at night.
4. Total Demand: The remaining electricity that the grid needs to supply after accounting for micro-generated solar power.

Spring, 2020: in the midst of on-line courses, due to the pandemic of Covid-19.

With the onset of the Covid-19 coronavirus crisis, we focus on SIRD models, which might realistically model the course of the disease.

We start with an SIR model, such as that featured in the MAA model featured in

Without mortality, with time measured in days, with infection rate 1/2, recovery rate 1/3, and initial infectious population I_0=1.27x10-4, we reproduce their figure

With a death rate of .005 (one two-hundredth of the infected per day), an infectivity rate of 0.5, and a recovery rate of .145 or so (takes about a week to recover), we get some pretty significant losses -- about 3.2% of the total population.

Resources:

Having a paid off house can feel secure, but home equity may be an expensive way to fund housing, compared to having a mortgage, or renting and saving the difference. This simulation compares the TCO (total cost of ownership) for renting and buying over a realistic time horizon.

To provide a brief overview of the description of this model, here is a table of contents of sorts:

- The Program - Overview

- The Model Itself - Macro Scale

- Principal Inputs

- Principal Outputs

- Inputs & Outputs - Brief Explanation

- The Details - How This All Works

- Viewing Data Outputs

The Program - Overview:

The model as seen revolves around the main variable components featuring "program" in their name and identified by their green color. The individual household starts at the "Building Envelope" program, which involves changes and modifications made to the actual building envelope of the house to make it more energy efficient, such as modifications to the insulation, windows, doors, etc. Then, the individual household will begin to progress through the behavioral component of the energy reduction program, starting with Climate Control. This portion concerns thermostat set points, the use of windows and fans to modulate temperature, and a behavioral adjustment in how to tolerate different levels of hot/cold temperature in the household. From here, the household moves through each room in the house, implementing energy reduction practices as appropriate. Because this program is designed to be modular and applicable to wide varieties of homes across the country, these rooms have been broken up into some standard categories that should apply to most households. These categories include the kitchen, the washroom, the main room/living room/"den", any bathrooms, and any bedrooms. Each category has its own set of energy reduction practices that can all be applied from a behavioral standpoint; clicking on each individual "program" will show a brief description of what these practices are in the notes section. Once the individual household has progressed through all of these areas, making the appropriate adjustments in each, they have more or less effectively "completed" the program. In reality, areas may continue to pop up where adjustments can be made to reduce energy consumption, so even though the program has been "completed" the members of the household will be continually working to maintain the new efficiency standard they have achieved with the end goal of cultivating a permanent, sustainable lifestyle. 

The Model Itself - Macro Scale:

The above is all essentially a description of how the household energy reduction program operates; the model is obviously tied to this, however it also includes an energy component that takes into account energy savings not only from a single house but all houses in a single community. How this all works will be discussed more in detail below, but first some basics will be gone over.

Principal inputs:

- energy capable of being saved in each portion of the program through behavioral changes (e.g. total possible energy reductions compared with initial baseline use prior to starting the program are X kWh/year and Y CCF/year)

- % of progress that needs to be made on meeting the reduction goal prior to moving onto the next program (e.g. for a total possible energy reduction of X compared to the initial energy use prior to starting the program, the participant must have reduced 90% of that total energy prior to moving on to the next program)

- time each program is projected to take (e.g. 4 weeks, 5 weeks, etc.)

- households in the community

- time (i.e. how long to run the model for, e.g. 52 weeks, 104 weeks, etc.)

Principal Outputs:

- amount of kWh of electricity saved by a household over the given period of time since starting the program (based on a kWh/yr basis)

- amount of CCF of gas saved by a household over the given period of time since starting the program (based on a CCF/yr basis)

- amount of gas and amount of electricity saved by the community the given period of time since starting the program (based on a per year basis)

- a plot of the progress made on each program for a specific period of time (e.g. which program is the household in, and what is their progress on the rest of the program they have already completed)

Inputs & Outputs - Brief Explanation:

For this model, the only inputs that could vary significantly from community to community are the specific number of households as well as the time the program has been in operation. Obviously the power that each household is capable of reducing can vary from household to household, however we are mainly concerned with the average energy reduction when looking at the community scale as there will always be outliers, which is why average numbers are used. Of the outputs produced, the kWh and CCF savings can be translated to lbs of CO2 saved, as well as other useful energy savings metrics that can better explain the impact of CE4A to the normal person than trying to explain the details behind what 1 kilo-watt hour is. Additionally, for a specific area's utility rate, the number of kWh/CCF saved overtime can yield data about how much money the specific household has saved since starting the program. This last statistic would be more helpful if the program were operating by strictly giving all the savings from energy reduction back to the homeowner; as this isn't exactly how CE4A handles this component, the model would have to be modified to more accurately depict the total savings going back to the homeowner/company revenue based on energy savings over time.

The Details - How This All Works:

Program Progression per Program:

The progress of the individual household through the home energy reduction program is essentially dictated by the progress through each individual program within. Progress through these individual programs is dictated by an inverse tangent curve that models behavioral change. The curve essentially outputs the % of progress the individual household has made, going from a value of 0 to 100. 

- Why an inverse tangent curve? - the shape of the curve includes an initial portion in which changes made are significantly large, followed by a portion in which the rate of change decreases as the easily made changes are completed over time. Compared with curves of similar shape, the important part about the inverse tangent curve is that it has a horizontal asymptote that the curve will only get close to, but never actually reach over time. This is representative of the concept that individuals will always have to work to maintain energy reduction practices until they become habit, as well as the reality that new challenges in the field of energy reduction can and will arise over time as people and technology changes.

***

Important to note: the inverse tangent function has been written to operate on a basis of weeks and % (in terms of a whole number XX.YY, not 0.XXYY). If the time scale is to be adjusted, say from weeks to months, then the entire tangent function must be rewritten to reflect this. Additionally, the function outputs values going from 0 to 100. This is a key reason why the function would need to be rewritten, as this would be drastically changed if different time units were used.

***

Inputs for each program include the progress % that the household needs to reach to advance on to the next program, as well as the time (in weeks) it should take them to reach this % threshold. Given the above explanation for how the inverse tangent curve works, the % progress and time threshold values should be chosen based on how much change is realistically possible within that time range (e.g. if it is realistic for an individual to complete 95% of possible changes within a 3 week period and form the habits to maintain those changes, then those values are well-suited for that program. However, if some programs have components that will take a long time to adapt to, then a longer period of time should be picked or a lower progress threshold, ideally the former.

Program Progression from Program to Program:

Each program following the first includes if-then statements related to the progress threshold of the previous program; once that program reaches that threshold, then the code the programs were written on will start the next program and reset its specific time scale to start at time=0 instead of time=current time in order to allow for flexibility in changing time thresholds without rewriting the entire inverse tangent function every time. In this way, changing progress thresholds not only affects the rate of progress of the current program but the start time of all others after it as well. 

Energy Reduction & Values:

The energy reduction numbers used in this model are all based on roughly what types of energy would be used in each room and how much is possible to be reduced. These numbers will all total up to the total projected energy reduction per household in terms of CCF/kWh, but the individual breakdown per room type as found in this model is entirely arbitrary and was chosen according to what made the most sense based on knowledge of what energy is used in which room and roughly how much with regards to the savings measures for the room type. These values are also on a per-week basis, so the small size is understandable in that context (originally on a yearly basis, then divided by 52 to get weeks to make this work with the model)

Original Use & Baseline Use:

Although this model does not utilize this and instead operates on a total savings possible basis, the initial energy usage of a particular household can be put into the "___.CCFOriginalUse" and "___.CCFBaseline" variables (note that CCF is interchangeable with KWH here) to get the total amount of possible savings based on real data. Currently, baseline use is set to 0 for each program with original use equivalent to the total amount of energy capable of being reduced per week for that room type. These numbers were derived from an estimate on the total energy reduction possible in terms of kWh and CCF, which was then broken down into each room and the type of energy capable of being reduced in each (see above section for more on this).

Note that "TotalKWH/CCFSavings" is for each individual household, whereas "NeighborhoodKWH/CCFSavings" is for the entire neighborhood composed of the amount of houses stored in the variable "#Households."

Viewing Data Outputs:

- Viewing current program progress at time X:

- use the plot option to while selecting "BuildingEnvelope.Program", "ClimateControl.Program", "Kitchen.Program," etc., to see the progress curves for each program over time.

- Viewing savings data:

- use the data table option to view the kWh/CCF savings over time for the household, the community, or both, changing the time column to display most recent time first; this will give the total savings in each of those areas for that entire time period.

Spring, 2020: in the midst of on-line courses, due to the pandemic of Covid-19.

With the onset of the Covid-19 coronavirus crisis, we focus on SIRD models, which might realistically model the course of the disease.

We start with an SIR model, such as that featured in the MAA model featured in

Without mortality, with time measured in days, with infection rate 1/2, recovery rate 1/3, and initial infectious population I_0=1.27x10-4, we reproduce their figure

With a death rate of .005 (one two-hundredth of the infected per day), an infectivity rate of 0.5, and a recovery rate of .145 or so (takes about a week to recover), we get some pretty significant losses -- about 3.2% of the total population.

Resources:

Added variables to simulate various climatic changes and natural disasters.