Measurement and Verification Baseline Energy Prridiction (MVBEP) Model Development Summary

This report summarizes the process of developing a MVBEP model and outputs the main highlights. Created on .

1. Initialization

1.1 Data Description

1.1.1 Metadata

The initialization process was started by the following inputs:

A historical data that extends from attrib_start_ to attrib_end_ with a frequency of .
The number of observations is .
The number of passed features is .
The building coountry code is .
The occupancy schedule is .

1.1.2 Descriptive Summary

1.2 Data Validation

This subsection validates the passed data based on the selected thresholds.

1.2.1 Timestamps

1.2.2 Remaining Features

1.3 Exploratory Data Analysis

1.4.1 Energy and outdoor dry-bulb temperature

1.4.2 Energy and invalid values

1.4.3 Outdoor dry-bulb temperature and invalid values

2. Transformation

2.1 Occupancy Schedule

2.2 Country Holiday

The following holidays were created using holiday library on Python.

3. Model Development

This section lists the trained and evaluated machine learning models in building a MVBEP model. The following are the passed parameters for the method develop_mvbep():

Hyperparameter tuning was .
The test set size is .

3.1 Model Evaluation Results

3.2 Actual Vs Predicted Energy consumption

3.2 Best Model

Based on the metric , the was selected with a frequency.