Ornithopter's Memory: Reactive Energy Optimization

Inspired by Frank Herbert's 'Dune' (where resource conservation is crucial) and Christopher Nolan's 'Memento' (fragmented memories driving actions), this project creates a 'reactive' energy management system. Imagine an Ornithopter (Dune) - constantly adjusting its wings to maximize efficiency. This system focuses on short-term reactive optimization and user awareness.

Story: Users wake up to a notification displaying a fragmented view of their energy consumption from the previous few hours, similar to 'Memento' - a single statistic, a highlighted appliance, or a time-window snapshot. These fragments are tailored to the user's specific consumption patterns. Over time, the system identifies recurring high-consumption periods (e.g., leaving lights on, using appliances simultaneously).

Concept: The system scrapes (like the 'Energy Consumption' scraper project) real-time energy consumption data from smart meters or smart plugs. It identifies deviations from learned baselines within short intervals (e.g., the last 3 hours, the last hour). Based on these deviations, it provides targeted, fragmented reminders and offers immediate reactive adjustments (e.g., dimming lights, switching off unused appliances via smart plugs). Think of it as a 'just-in-time' intervention strategy. The 'Memento' element reinforces the recent past, creating awareness and driving behavioral changes that are more effective than long-term energy reports.

How it works:
1. Data Acquisition: Scrape data from smart meters or connected appliances (smart plugs).
2. Baseline Learning: Establish a baseline energy consumption profile for different times of day and days of the week using historical data.
3. Anomaly Detection: Detect real-time deviations from the baseline within short intervals (e.g., last 30 minutes).
4. Fragment Generation: Create fragmented views of recent energy consumption, highlighting anomalies (e.g., 'Living Room TV on for 2 hours after 10 PM', 'Oven and Dryer running simultaneously').
5. User Notification: Deliver these fragments as notifications through a mobile app, encouraging immediate action (e.g., 'Turn off the TV', 'Delay Dryer cycle').
6. Reactive Adjustment: Allow users to directly control smart appliances through the app for immediate energy savings (turn off lights, adjust thermostat). The system can also automate some actions based on user-defined preferences (e.g. auto-dim lights after 10 pm).
7. Gamification: Integrate a points system based on proactive energy savings to increase user engagement. Points could unlock further automation features or custom themes for the app.

Low-Cost & Niche: The core system can be built using open-source tools (Python, MQTT) and low-cost hardware (Raspberry Pi, smart plugs). It targets users already using smart meters or interested in home automation.

High Earning Potential: The system can be monetized through:
- Subscription model: Offering premium features such as advanced automation rules, detailed consumption analysis, and integration with other smart home devices.
- Affiliate marketing: Recommending energy-efficient appliances and services.
- Data anonymization: Selling anonymized, aggregated energy consumption data to utility companies for grid optimization (with user consent).