on March 5th, 2021

In this paper, a new approach for load profile segmentation is investigated for residential energy consumption.

The proposed approach considers the daily level granularity and identifies dominant patterns of energy consumption for individual participants.

The analysis uses adaptive k-means clustering to determine the number of clusters that improve the distances between data points and cluster centroids.

The proposed method is applied to Ausgrid Solar Home Electricity Dataset for energy consumption data of 300 houses over 1 year.

The results demonstrate distinctive features including peak energy consumption, time of peak energy use, as well as seasonal variations.

The findings can help utilities to optimise demand response and pricing strategies.

Read More

The latest news

EIT News

8 Humanoid Robots Poised to Transform Industries and Engineering

These artificial intelligence (AI) bots backed by OpenAI, Tesla, and Amazon could be your new coworkers, and one of them is set to change how engineers do things. Humanoid robotics... Read more
EIT News

EIT Expands its Reach with the New Brisbane Campus Launch

Unlock the gateway to engineering excellence! EIT proudly unveils its new Brisbane campus, shaping the future of engineering education. Nestled in the vibrant heart of the city, join us in... Read more
EIT News

Revolutionizing Civil Engineering: The Impact of Artificial Intelligence

In the ever-changing field of construction, innovation has been a constant companion over the past two decades. As we progress further into 2024, the trajectory of the construction sector continues... Read more
Engineering Institute of Technology