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M3SA is setup using a json file. The Multi-Model is a top-layer applied on top of the
simulator,
capable to leverage into a singular tool the prediction of multiple models. The Meta-Model is a model generated from the
Multi-Model, and predicts using the predictions of individual models.
The Multi-Model's properties can be set using a JSON file. The JSON file must be linked to the scenario file and is
required
to follow the structure below.
## Schema
The schema for the scenario file is provided in [schema](M3SASchema.md)
In the following section, we describe the different components of the schema.
### General Structure
| Variable | Type | Required? | Default | Possible Answers | Description |
|------------------------|---------|-----------|---------------|-------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| multimodel | boolean | no | true | true, false | Whether or not to build a Multi-Model. If set to false, a Meta-Model will not be computed either. |
| metamodel | boolean | no | true | true, false | Whether to build a Meta-Model. |
| metric | string | yes | N/A | N/A | What metric to be analyzed from the computed files. |
| current_unit | string | no | "" | any string (e.g., "CO2", "Wh") | The international system unit of the metric to be analyzed, without prefixes. e.g., "W" for Watt is ok, "kW" is not. |
| unit_scaling_magnitude | integer | no | 10 | -9, -6, -3, 1, 3, 6, 9 | The scaling factor to be applied to the metric (10^-9, 10^-6, 10^3, 10^3, 10^6, 10^9). For no scaling, input 1. |
| window_size | integer | no | 1 | any positive, non-zero, integer | The size of the window, used for aggregating the chunks. |
| window_function | string | no | "mean" | "mean", "median" | The function used by the window for aggregating the chunks (e.g., for "mean", the window will compute the mean of the samples). |
| meta_function | string | no | "mean" | "mean", "median" | The function used by the Meta-Model to be generated. For "mean", the Meta-Model takes the mean of the individual models, at the granularity established by the window-size. |
| samples_per_minute | double | no | N/A | any positive, non-zero, double | The number of samples per minute, in the prediction data (simulator export rate). e.g., "0.2" means 1 sample every 5 minutes, "20" means a 20 samples per minute, or 1 sample every 3 seconds. |
| seed | integer | no | 0 | any integer >= 0 | The seed of the simulation. This must correspond to the seed from the output folder (from seed=x). |
| plot_type | string | no | "time_series" | "time_series", "cumulative", "cumulative_time_series" | The type of the plot, generated by the Multi-Model and Meta-Model. |
| plot_title | string | no | "" | any string | The title of the plot. |
| x_ticks_count | integer | no | None | any integer, larger than 0 | The number of ticks on x-axis. |
| y_ticks_count | integer | no | None | any integer, larger than 0 | The number of ticks on y-axis. |
| x_label | string | no | "Time" | any string | The label for the x-axis of the plot. |
| y_label | string | no | "Metric Unit" | any string | The label for the y-axis of the plot. |
| y_min | double | no | None | any positive, non-zero, double | The minimum value for the vertical axis of the plot. |
| y_max | double | no | None | any positive, non-zero, double | The maximum value for the vertical axis of the plot. |
| x_min | double | no | None | any positive, non-zero, double | The minimum value for the horizontal axis of the plot. |
| x_max | double | no | None | any positive, non-zero, double | The maximum value for the horizontal axis of the plot. |
## Examples
In the following section, we discuss several examples of M3SA setup files. Any setup file can be verified
using the JSON schema defined in [schema](M3SASchema.md).
### Simple
The simplest M3SA setup that can be provided to OpenDC is shown below:
```json
{
"metric": "power_draw"
}
```
This configuration creates a Multi-Model and Meta-Model on the power_draw. All the other parameters are handled by the
default values, towards reducing the complexity of the setup.
### Complex
A more complex M3SA setup, where the user has more control on teh generated output, is show below:
```json
{
"multimodel": true,
"metamodel": false,
"metric": "carbon_emission",
"window_size": 10,
"window_function": "median",
"metamodel_function": "mean",
"samples_per_minute": 0.2,
"unit_scaling_magnitude": 1000,
"current_unit": "gCO2",
"seed": 0,
"plot_type": "cumulative_time_series",
"plot_title": "Carbon Emission Prediction",
"x_label": "Time [days]",
"y_label": "Carbon Emission [gCO2/kWh]",
"x_min": 0,
"x_max": 200,
"y_min": 500,
"y_max": 1000,
"x_ticks_count": 3,
"y_ticks_count": 3
}
```
This configuration creates a Multi-Model and a Meta-Model which predicts the carbon_emission. The window size is 10, and
the aggregation function (for the window) is median. The Meta-Model function is mean. The data has been exported at a
rate of 0.2 samples per minute (i.e., a sample every 5 minutes). The plot type is cummulative_time_series, which starts
from a y-axis value of 500 and goes up to 1000. Therefore, the Multi-Model and the Meta-Model will show only
the values greater than y_min (500) and smaller than y_max (1000). Also, the x-axis will start from 0 and go up to 200,
with 3 ticks on the x-axis and 3 ticks on the y-axis.
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