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Automated Real-Time Interactive Architecture

Introduction

Maintaining a comfortable environment within a building such as a home or a school can often be a burden on homeowners and maintenance staff. Adjustment of the temperature and lighting in a room, for instance, is often a repetitive task which it would be convenient to automate.

This report contains a proposal for a project to eliminate these manual tasks. The objective of the project is to build a system to automate control of a building's environment, which uses machine learning algorithms to minimize manual configuration. The proposal presents several motivations for using the environmental control system, as well as a detailed description of the project's objectives. The proposal also includes a timeline for the completion of concrete milestones for the project as well as a technical summary of the proposed solution.

Background

Any home maintenance task which can be automated can save the owner a substantial amount of time and money. Automated environmental control is not a novel concept; devices such as light timers and programmable thermostats have existed for many years. Most of these common devices, however, must be configured manually. The system proposed in this report is able to configure itself based on normal actions taken by the user. By having the system learn the habits of the user dynamically, the configuration is essentially eliminated, leading to an ease of installation that does not currently exist.

One scenario in which a home automation system could be a significant help is in the case of a homeowner with physical limitations that prevent them from independently maintaining their house. A system which controls the living environment without requiring substantial configuration can give these people more independence, or allow their caregivers to focus on other priorities.

There is also a place for automated environment control in a commercial setting. An example of this is in educational buildings, where there are thousands of people in many different rooms at all times of day. Maintaining a single building in this situation requires full time staff. For example, during a lecture at sunset, an instructor may have to adjust the blinds and contact the maintenance staff to adjust the temperature at the same time each day. The cost and effort of this maintenance is multiplied for every building in a given institution. If a system could be installed in each building that learns to handle this activity automatically, it would help reduce the burden and cost of maintenance.

Another benefit of automated environment control is improved energy efficiency. It is not uncommon to leave a room forgetting to turn off a light, or to leave a building with the air conditioning still running. This leads to needless consumption of energy, again increasing the cost of maintaining the building. Removing this responsibility from the building owner also removes this waste of energy. An automated environment control can also improve energy efficiency beyond just removing negligence. Once the desired temperature of a room for a given time is known, options besides air conditioning or heating can be explored first. If the room temperature must be raised, and the temperature outside is warm enough, the system could open the room windows. Once the desired temperature has been reached, the windows could be automatically shut again.

Objectives

Currently, supervised learning algorithms require a complete input dataset and a meticulously configured output to train the system. We want to have the algorithm be trained in a real world environment where users perform live actions and the system determines relevant changes and environmental factors. This automated learning system will be generic and use only the categorical and numerical information provided by sensors and actuators.

Manual adjustment of a building's environment can be tedious. A goal of the project will be to create a system which uses automated learning to adjust the environment based on input from a number of sensors. The system should support simple configuration of any type of sensor, and should not contain detailed knowledge about any sensors or actuators.

This system will support dynamic updates to the configuration of sensors and actuators. Overhead for adding and removing new devices from the system will be minimal. The learning algorithm will then adjust to take these new devices into account when altering or maintaining the environment.