Building Energy Signature Recording and Forecasting

Buildings are the dominant source of energy consumption in urban areas. Therefore, the ability to forecast and characterize building energy consumption is vital for implementing urban energy management and efficient initiatives required to control energy wastage. Developments in smart metering techn

Project Title

Building Energy Signature Recording and Forecasting

Project Area of Specialization

Electrical/Electronic Engineering

Project Summary

Buildings are the dominant source of energy consumption in urban areas. Therefore, the ability to forecast and characterize building energy consumption is vital for implementing urban energy management and efficient initiatives required to control energy wastage. Developments in smart metering technology have enabled researchers to develop “sensor based” approaches to forecast building energy consumption that necessitate less input data than traditional methods. Sensor-based forecasting utilizes machine learning techniques to infer the complex relationships between consumption and influencing variables (e.g., weather, time of day, previous consumption). While sensor-based forecasting has been studied extensively for commercial buildings, there is a paucity of research applying this data-driven approach to the multi-family residential sector. In this project we will build a sensor-based forecasting model using embedded systems and machine learning techniques to successfully transfer the data in real time on cloud and then using forecasting of acquired data to automate the equipment’s according to requirements.

Project Objectives

• To successfully make embedded system hardware containing various sensors.
• To connect hardware with the cloud and collect data in real time.
• To automate the equipment’s based on collected data.
• To use machine learning algorithms for predictions based on collected data for one month.
• To successfully establish an application or website that show real time data and values.

Project Implementation Method

This project has three major portion to be implemented such as Energy signature, recording and forecasting.

Energy siignature is a technique for measurment of energy consumption in multi-family and multi floor buildings.first we will use a techinque to measure energy consumption of building using ESA.

In second phase we will record the energy consumption for atleast one month using our hardware which will contain different sensors and microcontrollers wihch will be made using Iot techniques.

In final phase of our project we will forcast the energy consumption of building on basis of previous data recorded using AI algorithm

Benefits of the Project

• Controlled energy consumption of multi family buildings
• Real time monitoring of energy consumption
• Easy access and user friendly GUI for customers
• Cost reduction of energy usage
• Accurate forecasting of energy consumption

Technical Details of Final Deliverable

Our final deliverable will consist of a Hardware device as well as Applicaiton and website which will update data in real time which will be recorded by different sensors of our hardware.

Hardware will be a medium size PCB consisting of sensors, microcontrollers, network card, USB powered input supply, and miscellaneous.

Final Deliverable of the Project

HW/SW integrated system

Core Industry

Energy

Other Industries

Core Technology

Internet of Things (IoT)

Other Technologies

Artificial Intelligence(AI), Cloud Infrastructure

Sustainable Development Goals

Affordable and Clean Energy

Required Resources

If you need this project, please contact me on contact@adikhanofficial.com