README.md

Appliances Energy Prediction 💡

Testing Multiple Regression Models to Predict Energy Efficiency of Buildings.

Multiple linear regression establishes the relationship between the target variable and the predictors (usually two or more). In reality, several factors contribute to a certain outcome as opposed to just one as suggested by simple linear regression. Multiple linear regression has similar assumptions as simple linear regression and also assumes that there is no significant correlation between the predictors. While the relationship between variables can be linear, it allows for non-linear relationships that are not straight lines.

Dataset 💾

The dataset is Appliances Energy Prediction data. The data is time series data at 10 min intervals for about 4.5 months. The house temperature and humidity conditions were monitored with a ZigBee wireless sensor network. Each wireless node transmitted the temperature and humidity conditions around 3.3 min. Then, the wireless data was averaged for 10 minutes periods. The energy data was logged every 10 minutes with m-bus energy meters. Weather from the nearest airport weather station (Chievres Airport, Belgium) was downloaded from a public data set from Reliable Prognosis (rp5.ru), and merged together with the experimental data sets using the date and time column.

Two random variables have been included in the data set for testing the regression models and to filter out non predictive attributes (parameters). The attribute information can be seen below.

Feature Name	Description	Data Type
date	year-month-day hour:minute:second	datetime64[ns]
Appliances	energy use in Wh	int64
lights	energy use of light fixtures in the house in Wh	int64
T1	Temperature in kitchen area, in Celsius	float64
RH_1	Humidity in kitchen area, in %	float64
T2	Temperature in living room area, in Celsius	float64
RH_2	Humidity in living room area, in %	float64
T3	Temperature in laundry room area	float64
RH_3	Humidity in laundry room area, in %	float64
T4	Temperature in office room, in Celsius	float64
RH_4	Humidity in office room, in %	float64
T5	Temperature in bathroom, in Celsius	float64
RH_5	Humidity in bathroom, in %	float64
T6	Temperature outside the building (north side), in Celsius	float64
RH_6	Humidity outside the building (north side), in %	float64
T7	Temperature in ironing room , in Celsius	float64
RH_7	Humidity in ironing room, in %	float64
T8	Temperature in teenager room 2, in Celsius	float64
RH_8	Humidity in teenager room 2, in %	float64
T9	Temperature in parents room, in Celsius	float64
RH_9	Humidity in parents room, in %	float64
T_out	Temperature outside (from Chievres weather station), in Celsius	float64
Press_mm_hg	Pressure (from Chievres weather station), in mm Hg	float64
RH_out	Humidity outside (from Chievres weather station), in %	float64
Windspeed	Wind speed (from Chievres weather station), in m/s	float64
Visibility	Visibility (from Chievres weather station), in km	float64
Tdewpoint	Tdewpoint (from Chievres weather station), in °C	float64
rv1	Random variable 1, nondimensional	float64
rv2	Random variable 2, nondimensional	float64

How to Use The Repository

You need to have Python 3 on your system. Then you can clone this repo and being at the repo's root :: repository_name> ...

1. Clone this repository: git clone https://github.com/Azie88/Regression-Energy-Data
2. On your IDE, create A Virtual Environment and Install the required packages for the project:

• Windows:

    python -m venv venv; 
    venv\Scripts\activate; 
    python -m pip install -q --upgrade pip; 
    python -m pip install -qr requirements.txt  
  

• Linux & MacOs:

    python3 -m venv venv; 
    source venv/bin/activate; 
    python -m pip install -q --upgrade pip; 
    python -m pip install -qr requirements.txt  
  

The two long command-lines have the same structure. They pipe multiple commands using the symbol ; but you can manually execute them one after the other.

• Create the Python's virtual environment that isolates the required libraries of the project to avoid conflicts;
• Activate the Python's virtual environment so that the Python kernel & libraries will be those of the isolated environment;
• Upgrade Pip, the installed libraries/packages manager to have the up-to-date version that will work correctly;
• Install the required libraries/packages listed in the requirements.txt file so that they can be imported into the python script and notebook without any issue.

NB: For MacOs users, please install Xcode if you have an issue.

3. Explore the Jupyter notebook for detailed steps and code execution.

Author ✍️

Andrew Obando

---

Feel free to star ⭐ this repository if you find it helpful!