Energy Industry : Leveraging Time Series Data Analytics to Revolutionise Energy and Utilities Industry

Energy Industry : Leveraging Time Series Data Analytics to Revolutionise Energy and Utilities Industry

Our team is at the forefront of implementing advanced data analytics solutions for the energy and utilities industry. We have successfully leveraged time series data to remotely monitor partial discharge on oil fields and industrial machinery. By building custom classifiers using deep learning and machine learning algorithms, we have enabled customers to take remote action to protect their expensive machinery based on severity of the partial discharge.

Ground truth is not always available for most AI problems. Therefore, our team designed an Annotator tool for domain experts using PySimpleGUI, which enables experts to annotate the data and send it directly to the Azure cloud.

Our solutions also extend to monitoring power usage patterns and identifying typical and atypical days based on energy usage using custom distance metric and agglomerative clustering algorithms. We have also developed a Variational Autoencoder model to detect active cooking events and identify power anomalies in HVAC systems using a combination of research and AI algorithms.

Our team has also implemented an energy recommendation system that suggests optimal times to operate costly appliances based on the user’s goal to save electricity costs and tariff rate.

We have also utilised Python to create a simulator that tracks optimal paths during power outages and recommends them to utilities to minimise and prevent downtime, thereby saving costs. Our innovative solutions stand as a testament to our ability to leverage cutting-edge technology to deliver unprecedented results in the energy and utilities industry.

Problem Statement

The energy and utilities industry faces challenges related to remote monitoring of partial discharge on oil fields and industrial machinery, identifying power usage patterns, detecting active cooking events, recommending optimal times for appliance usage, and minimising power outages.

Solution

Our team has implemented advanced data analytics solutions that leverage time series data to remotely monitor partial discharge on oil fields and industrial machinery. We have built custom classifiers using deep learning and machine learning algorithms to enable customers to take remote action to protect their expensive machinery based on severity of the partial discharge. Additionally, we have developed custom distance metric and agglomerative clustering algorithms to monitor power usage patterns and identify typical and atypical days based on energy usage. We have also designed a Variational Auto-encoder model to detect active cooking events and identify power anomalies in HVAC systems. Furthermore, we have developed an energy recommendation system that suggests optimal times to operate costly appliances based on the user’s goal to save electricity costs and tariff rate. Lastly, we have utilised Python to create a simulator that tracks optimal paths during power outages and recommends them to utilities to minimise and prevent downtime, thereby saving costs. Our innovative solutions demonstrate our ability to leverage cutting-edge technology to deliver unprecedented results in the energy and utilities industry.

Project Tasks

  • Classification Models

    Built custom classifiers using deep learning and machine learning algorithms to enable customers to take remote action.

  • Recommendation System

    Suggests optimal times to operate costly appliances based on the user’s goal to save electricity costs and tariff rate.

Project Details

Development of custom classifiers using deep learning and machine learning algorithms to enable customers to take remote action to protect their expensive machinery based on severity of the partial discharge. Development of custom distance metric and agglomerative clustering algorithms to monitor power usage patterns and identify typical and atypical days based on energy usage. Design of Variational Auto-encoder model to detect active cooking events and identify power anomalies in HVAC systems.

Let’s Talk About Your Project

Let’s Talk About Your Project

With vast experience in harnessing the latest AI technologies in crafting solutions, we would value a conversation with you about your business and how Keynet AI may guide you through your AI education and implementation journey.