Entropy Audit Reversal (EAR)

Inspired by the 'approval workflows' scraper's ability to automate processes, 'I, Robot's' proactive AI, and 'Tenet's' temporal inversion concept, EAR aims to create a self-optimizing energy management system. The core concept is to model expected energy consumption patterns for a given facility (e.g., a small factory, a data center, or a large office building) using historical data and real-time sensor readings (temperature, occupancy, equipment status, power draw of individual devices etc.).

The 'Entropy Audit' component uses AI (specifically, anomaly detection algorithms such as autoencoders or time-series forecasting models) to continuously monitor deviations from these predicted patterns. These deviations indicate potential energy waste or inefficiencies. The 'Reversal' aspect comes from the AI predicting future deviations based on current trends (similar to 'Tenet' anticipating future events) and suggesting corrective actions before the inefficiencies become significant.

Here's how it works:

1. Data Acquisition: Collect energy consumption data from smart meters, IoT sensors, and building management systems. This can be implemented using readily available open-source libraries and APIs. A low-cost Raspberry Pi can serve as the edge computing device.
2. Baseline Modeling: Train a machine learning model (e.g., a Long Short-Term Memory (LSTM) network or a Seasonal ARIMA model) to predict expected energy consumption based on historical data and various contextual factors (time of day, weather, occupancy, etc.).
3. Anomaly Detection: Continuously compare the predicted energy consumption with the actual consumption. Use anomaly detection algorithms to identify significant deviations (e.g., exceeding a certain threshold or falling outside a confidence interval).
4. Proactive Correction (The 'Reversal' aspect): When an anomaly is detected, the system analyzes the data to identify the likely cause (e.g., a malfunctioning HVAC unit, an overloaded circuit, or a room with lights left on after hours). Based on this analysis, the system suggests corrective actions to the facility manager or even automatically triggers them (e.g., adjusting thermostat settings, turning off lights, or sending an alert to maintenance personnel).
5. Workflow Automation (Inspired by Approval Workflows): Implement an approval workflow system (e.g., using a lightweight workflow engine like Camunda or Activiti) to manage the corrective actions. This allows for human oversight and ensures that critical changes are reviewed and approved before being implemented. Example: Suggest to shut down a device that is using high power while the sensor report it is not in use. Then the request can be accepted and/or rejected by the supervisor via a mobile app.
6. Continuous Learning: The system continuously learns from the data and refines its models, improving its accuracy in predicting and preventing energy waste.

Niche: Focus on specific industries (e.g., data centers) or types of buildings (e.g., small factories) to tailor the system to their unique energy consumption patterns and needs.

Low-Cost: Utilize open-source software, readily available hardware (Raspberry Pi, sensors), and cloud-based services to minimize development and operational costs.

High Earning Potential: The system can be offered as a subscription-based service, with different pricing tiers based on the size and complexity of the facility. The potential for cost savings in energy consumption makes the system highly valuable to businesses, resulting in a potentially high return on investment for the developers.