Real-Time Electricity Consumption ML Engineer
Budget: $30 – $250 USD
I'm in need of a skilled Machine Learning Engineer who can develop modules to help analyze the real-time electricity consumption for a variety of devices in our system.
Backend:
Key Points:
- Devices to be analyzed include air conditioners, lights, and simple motors and other simple devices.
- The analysis should cover both the collective consumption of groups of devices and the individual consumption patterns of each device.
Ideal Experience:
- Strong background in Machine Learning, ideally with experience in real-time data analysis.
- Previous work in energy consumption analysis or similar fields would be a huge plus.
- Proven track record in developing ML models for group and individual level analysis.
The ML analysis we are looking for includes:
- Identifying energy usage patterns to help us understand consumption trends.
- Implementing anomaly detection techniques to flag any irregular or unexpected behavior in our system.
- Providing actionable recommendations for energy optimization based on the patterns and anomalies identified.
Details about the data:
I want to have 3 tables:
Real time data table:
The data will be captured directly from sensors at the form of columns, for example, Device 1, device 2 , device 3, and rows are time captured at growing rate of 1s. the value of the row is the energy consumed of that device at that exact second. This will be a large table.
Hourly Data Table: Aggregate data hourly to feed into the anomaly detection model, stored in the 'Hourly' table, to identify significant deviations.
Daily Data Table: Summarize daily data for use with the SARIMA model, aiding in long-term consumption predictions and trends analysis.
All the tables are fed from the main large table (secondly rate table - real time table).
Details about the models:
ML Model Development:
Anomaly Detection Model: Develop a model using Random Forest algorithms to detect unusual patterns in energy usage.
SARIMA Forecasting Model: Implement SARIMA models to forecast daily energy requirements, considering seasonal variations.
Database:
A cloud SQL real time database is preferred.
Front end:
After analyzing the data, I want to display the results to the user in Flutter with a very simple UI.
I want to display:
- Total energy consumption.
- A graph for the total energy consumption by hour.
- Energy consumed for each device.
- Energy consumption compared to yesterday's.
- Estimated energy consumption by end of the month.
- Recommendations for energy optimization.
If you have the right skills, experience, and a passion for developing innovative solutions in the energy sector, I would love to hear from you.
Backend:
Key Points:
- Devices to be analyzed include air conditioners, lights, and simple motors and other simple devices.
- The analysis should cover both the collective consumption of groups of devices and the individual consumption patterns of each device.
Ideal Experience:
- Strong background in Machine Learning, ideally with experience in real-time data analysis.
- Previous work in energy consumption analysis or similar fields would be a huge plus.
- Proven track record in developing ML models for group and individual level analysis.
The ML analysis we are looking for includes:
- Identifying energy usage patterns to help us understand consumption trends.
- Implementing anomaly detection techniques to flag any irregular or unexpected behavior in our system.
- Providing actionable recommendations for energy optimization based on the patterns and anomalies identified.
Details about the data:
I want to have 3 tables:
Real time data table:
The data will be captured directly from sensors at the form of columns, for example, Device 1, device 2 , device 3, and rows are time captured at growing rate of 1s. the value of the row is the energy consumed of that device at that exact second. This will be a large table.
Hourly Data Table: Aggregate data hourly to feed into the anomaly detection model, stored in the 'Hourly' table, to identify significant deviations.
Daily Data Table: Summarize daily data for use with the SARIMA model, aiding in long-term consumption predictions and trends analysis.
All the tables are fed from the main large table (secondly rate table - real time table).
Details about the models:
ML Model Development:
Anomaly Detection Model: Develop a model using Random Forest algorithms to detect unusual patterns in energy usage.
SARIMA Forecasting Model: Implement SARIMA models to forecast daily energy requirements, considering seasonal variations.
Database:
A cloud SQL real time database is preferred.
Front end:
After analyzing the data, I want to display the results to the user in Flutter with a very simple UI.
I want to display:
- Total energy consumption.
- A graph for the total energy consumption by hour.
- Energy consumed for each device.
- Energy consumption compared to yesterday's.
- Estimated energy consumption by end of the month.
- Recommendations for energy optimization.
If you have the right skills, experience, and a passion for developing innovative solutions in the energy sector, I would love to hear from you.