A Behavioral-Based Mathematical Modeling and Predictive System for Residential Electricity Consumption Optimization

The Big Idea : Normally, when electricity companies or engineers try to guess how much power a house will use, they just look at the appliances: how many watts the air conditioner or TV consumes, and how many hours they run. But they completely forget one unpredictable thing: human behavior. This research is all about shifting the focus from just "machines" to "human habits." People waste a huge amount of energy without realizing it, either by leaving devices plugged in all the time (which causes Vampire Power or Phantom Load) or by leaving high-power electronics like ACs and lights running in empty rooms. My project builds a mathematical and computer-based system to measure and predict exactly how much these daily human habits affect our electricity bills. What I Did: Real-World Data: I didn’t just guess the numbers. I conducted a real field survey across 32 local households here in Egypt to see what people actually do. I found out that more than half of the people leave their AC main switches connected all winter, and around 40 percent routinely leave devices on standby mode. The Math and Python Code: Using this real data, I created a formula called the Behavior Factor. Then, I wrote a Python program (using NumPy and Pandas) to simulate and compare three types of people: a Conscious user (who unplugs everything), an Average user, and a Negligent user (who leaves everything running). The Egyptian Tariff System: To make the results practical, I programmed the exact, non-linear tiered tariff system used by the Egyptian electricity grid. This helps show how bad habits can suddenly push a household into a much higher, more expensive billing slab. The Key Findings: The results were eye-opening. A negligent user wastes so much energy that their behavior-driven waste makes up about 37.5 percent of their total electricity bill. The study proves that just by changing our daily habits, without buying any expensive new appliances, a household can cut its electricity consumption by up to 35.1 percent and avoid heavy financial penalties from the utility company. Future Goals: This project is designed to be the brain for future Smart Homes. Because the equations are lightweight, they can easily run on cheap microcontrollers (like ESP32 smart plugs) to automatically cut off vampire power, or use Machine Learning to study a family's habits and alert them before their electricity bill skyrockets.