The control of heating and ventilation in indoor environments is vital for establishing a comfortable atmosphere that maximizes productivity and supports health. However, heating and ventilation consume a lot of energy in a building. Building consumes approximately 38% of the energy used in the U.S. (Department of Energy United States of America). We have developed a low-cost microcontroller control algorithm to resolve this issue by optimizing energy consumption. This control algorithm is designed to enable user-defined control and direct control ventilation of the heating and ventilation operation of a mock building. This algorithm helps control 1) heating off, 2) heating on with two electric heating pads, 3) ventilation off, and 4) ventilation on with fan speed increasing. With this algorithm's successful implementation, heating and ventilation management can become fully autonomous based on factors such as carbon dioxide levels and temperature inside a mock building. An example of this on a larger scale can be seen with artificial intelligence, which uses its user data to decrease power consumption and increase comfort (Chow). 

Building heating is usually maintained at uncomfortable temperatures; therefore, humans need thermal comfort to perform better at work or at home. Heating is generally controlled by a switch and/or sensors that include a deadband, meaning the heater turns on at a specific temperature. Once a certain maximum temperature is reached, the heater will turn off. Then, the heater will only turn back on until the specific lowest temperature is reached. Ventilation is essential because indoor air is more polluted than outside air (United States Environmental Protection Agency). Ventilation in buildings is usually poor, primarily in outdated buildings. Ventilation in buildings is controlled using a switch, such as ceiling fans or outdoor exhaust in bathrooms. Humans are a main source of CO2 levels inside a building, emitting carbon dioxide (CO2). Within these two units (heating and ventilation), our group set out to create a mock building that can operate autonomously to keep the temperature within a set deadband via heat from two resistive heaters and a mixing fan, and have adequate ventilation from an output fan dispersing the polluted air inside the room. Our group successfully implemented these features in our mock building and gathered data about the changing temperature and CO2 levels inside the box.

Controlling lighting in indoor environments is vital for establishing a comfortable atmosphere that maximizes productivity and supports health. It is a fact that lighting constitutes a substantial portion of energy consumption in buildings, which collectively account for about 38% of energy use in the U.S. (Department of Energy, United States of America). To tackle this issue, we have developed a robust control algorithm using a low-cost microcontroller that allows users to customize lighting operations in a physical room model. This sophisticated algorithm operates in four distinct modes: 1) off, 2) constantly on, 3) activated by activity detected through an ultrasonic sensor, and 4) triggered by input from a photoresistor. With the successful implementation of this algorithm, users can effectively manage lighting in their spaces. An example of this on a larger scale in commercial buildings can be seen by using AI to control lighting and ventilation, which has been proven to reduce power consumption (Chow). Our voltage drop measurements and current readings from the Neopixel confirm it consumes 2 W at full brightness when displaying blue light continuously for 24 hours

Indoor lighting is important because every building requires lighting, which requires energy. Energy is drawn by commercial buildings, totaling 4.35% of energy consumption (Department of Energy, United States of America). A light switch typically manages indoor lighting in residential buildings. Currently, sensors and artificial intelligence are being implemented to manage lighting systems in commercial buildings (Siemens). This technology can help reduce energy consumption by depending less on human mistakes, such as forgetting to switch off the lights when rooms are not in use. Automating systems to control lighting is essential because it can minimize power consumption, which can lead to a lower energy bill, and minimize light pollution and unnecessary use of power. This can result in a cheap process because most of the hardware is already in place, and most software is needed (Chow).

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