Balai: iot power and water management system with ai for mode-control, recommendations and capping mechanism/ Kathleen T. Amador, Adrian Gabriel B. Arcilla, Paul Christian M. Castillo, Margaret Zena A. Dalire, Rein Kirsten E. Solpico, and Gino D. Vidal.--

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Amador, Kathleen T [author]

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Material type: Text

TextPublication details: Manila: Technological University of the Philippines, 2025.Description: xii, 95pages: 29cmContent type:

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BTH TK 870 A43 2025

Dissertation note: College Of Engineering.-- Bachelor of science in electronics engineering: Technological University of the Philippines, 2025. Summary: BalAI: IoT Power and Water Management System with AI for Mode-Control, Recommendations, and Capping Mechanism introduces a streamlining approach to household utility regulation within a rapidly growing urban environment and booming demand for core needs. Most of the traditional household utility infrastructures remain traditional or non-monitored and rarely have facility to communicate in bidirectional manner, resulting in inefficient resource utilization and high consumption patterns. A lot of solutions in smart home technologies work in isolation and do not provide intuitive, smart control based on the behavior of users. In addition, low cost and access in development areas still raise major issues. Two separate monitoring devices were built using ESP32 microcontrollers. The power module incorporated sensors such as ZMPT101B for voltage and ACS712 for current, while the water system used a YF-S201 flow sensor and a motorized solenoid valve. Both modules were mounted on a custom PCB and supported by relay control and voltage regulation. A Flutter-Dart Language and Firebase based mobile application enabled control, monitoring and notifications. Moreover, a PPO (Proximal Policy Optimization) reinforcement learning model was carried out by using TensorFlow.js. The system was tested from mid-February to mid-April, learning and responding with data and feedback in real time. The figures speak for themselves: 8 cubic meters of water and Php 348.7 worth of electricity were saved using optimized usage caps and AI-generated advice. It also got better over time, learning from what users did. BalAI proves that a locally adaptable, AI-integrated solution can empower households to manage utilities effectively, reduce waste, and support sustainable living through practical, user- friendly technology.

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Holdings
Item type	Current library	Shelving location	Call number	Copy number	Status	Date due	Barcode
Bachelor's Thesis COE	TUP Manila Library	Thesis Section-2nd floor	BTH TK 870 A43 2025 (Browse shelf(Opens below))	c.1	Not for loan		BTH0006436

Bachelor's thesis

College Of Engineering.--
Bachelor of science in electronics engineering: Technological University of the Philippines,
2025.

Includes bibliographic references and index.

BalAI: IoT Power and Water Management System with AI for Mode-Control,
Recommendations, and Capping Mechanism introduces a streamlining approach to
household utility regulation within a rapidly growing urban environment and booming
demand for core needs. Most of the traditional household utility infrastructures remain
traditional or non-monitored and rarely have facility to communicate in bidirectional
manner, resulting in inefficient resource utilization and high consumption patterns. A lot of
solutions in smart home technologies work in isolation and do not provide intuitive, smart
control based on the behavior of users. In addition, low cost and access in development
areas still raise major issues. Two separate monitoring devices were built using ESP32
microcontrollers. The power module incorporated sensors such as ZMPT101B for voltage
and ACS712 for current, while the water system used a YF-S201 flow sensor and a
motorized solenoid valve. Both modules were mounted on a custom PCB and supported
by relay control and voltage regulation. A Flutter-Dart Language and Firebase based mobile
application enabled control, monitoring and notifications. Moreover, a PPO (Proximal
Policy Optimization) reinforcement learning model was carried out by using
TensorFlow.js. The system was tested from mid-February to mid-April, learning and
responding with data and feedback in real time. The figures speak for themselves: 8 cubic
meters of water and Php 348.7 worth of electricity were saved using optimized usage caps
and AI-generated advice. It also got better over time, learning from what users did. BalAI
proves that a locally adaptable, AI-integrated solution can empower households to manage
utilities effectively, reduce waste, and support sustainable living through practical, user-
friendly technology.

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