Household-Power-Consumption

Introduction

With the advent of new gadgets and a push towards greater electrification projects globally, power consumption is rising globally. Thus, we can also expect that household or residential power consumption is so on the rise. With greater access to global power consumption data, forecasting power consumption is an emerging challenge. An accurate forecast can help both the consumer as well as the supplier side. For the consumer, a power forecast helps in financial planning as making more green choices overall. For the supplier, an accurate forecast will definitely help in supply regulation. Thus, such models can help to optimize the overall supply chain of the household power industry.

Project Goal:

Track the power consumption of individual households in almost real time.

Used Language and Libraries:

└── Python
      ├── Numpy   
      ├── Pandas
      ├── Matplotlib
      ├── Seaborn
      ├── Sklearn
      ├── datetime
      └── missingno   
       

Dataset

Measurements of electric power consumption in one household with a one-minute sampling rate over a period of almost 4 years. Different electrical quantities and some sub-metering values are available.

Associated Tasks:

Regression

Data Set Information:

This archive contains 185711 (sample of original dataset) measurements gathered between December 2006 and November 2010 (47 months).

Project Team :

-Mona Mohammed Hamdy.
-Manar Hamada Elsayed.
-Ahmed Mohammed Sanad.
-Adham Ashraf Elganzoury.
-Khaled Abdelmohsen Sayed.
-Ahmed Mohammed Kassem.