"EnergyClusterAnalytics" is a cutting-edge academic project, that utilizes unsupervised learning techniques to delve into and understand patterns in residential electricity consumption . This project applies Principal Component Analysis (PCA), various clustering algorithms, and Binary Segmentation Search to identify distinct consumption behaviors across 100 apartments over a span of 91 days. Through detailed data analysis, we aim to uncover insights for more targeted and efficient energy management strategies 💡.
- To explore and understand the variability in electricity consumption among different households.
- To apply unsupervised learning methods to segment electricity consumption data into homogenous clusters.
- To identify significant shifts in consumption patterns, enabling the development of personalized energy-saving strategies.
The dataset comprises half-hourly electricity usage data from 100 apartments, collected over 91 consecutive days. It includes measurements of energy consumption, with additional details on the apartment number and the day of recording, providing a comprehensive view of residential energy utilization patterns.
- Data Exploration: Initial examination of the dataset to understand its structure and prepare it for analysis.
- Dimensionality Reduction: Application of PCA to reduce the complexity of the data while retaining significant information.
- Clustering: Implementation of clustering algorithms to group similar patterns of electricity consumption.
- Segmentation: Detailed analysis of segmented data to distinguish unique consumption behaviors.
- Binary Segmentation Search: Identification of points indicating significant changes in consumption patterns over the observed period.