Mr Ahmed Al-Adaileh

Research project: Integrated Scalable System for Smart Energy Management


The planet's reserves are encountering vital challenges and suffer inequitable consumption. The outcomes of the prostration of natural reserves have started affecting every single organism on the globe. Energy is a critical key-factor in this aspect because a considerable part of the destruction is triggered by utilizing the planet reserves to produce power in diverse forms. Enormous endeavour in energy management will be needed in order to revert the situation to its appropriate track; these efforts have to be focused on two main divisions: producing electricity from clean and renewable reserves and decreasing the depletion of the total generated energy. The increasing environmental awareness in humans' minds, and the rapid development of smart concepts, home automation technologies in both hardware and software fields, played an essential rule in speeding up the progress to apply smart energy management. The focus of this work is building a smart, scalable system that can be applied in any environment starting from small household units to big organisations with thousands of appliances, offering them smart energy management which meets the most relevant quality attributes such as security, scalability, interoperability, availability and adaptability. IoT concepts will be applied to connect the physical world appliances to the Internet using a farm of cloud-based microservices that interacts with BigData analytics algorithms to meet the growing data volume, variety and veracity within acceptable velocity, and to monitor and predict energy consumption.


I completed my MA in Software Engineering at Kingston University, London which led to my research in designing an integrated scalable system to manage energy consumption in household sector using different smart home, machine learning, deep learning and IoT technologies. I have been involved in projects in Germany to develop RESTful applications using Java, and Z-Wave protocol to connect classical household appliances to a local network in order to offer some basic control commands and tracking possibilities. These were projects to observe and get in touch with all technologies and techniques evolved in thie area in the last decade.

Currently, I enjoy constructing several Raspberry pi and Arduino-based special devices to measure, track and register several internal and external parameters. Moreover, different software such as Arduin IDE, Python, Java Spring and famous libraries such as Open CV are utilized to perform image processing and face recognition and identification tasks.

Areas of research interest

  • Smart home technologies
  • Deep learning
  • Energy management
  • Big data and data mining techniques
  • Internet of Things
  • Cloud computing
  • Machine learning technoiques


  • Master in Software Engineering
  • Bachelor in Computer Science Engineering

Funding or awards received

  • Best Master Dissertation from Kingston University - London
  • Best presentation award of my paper published in the ICSEMEE 2020: 14. International Conference on Smart Energy Management and Energy Efficiency, in Paris, France


  • Reduction of Energy Consumption Using Smart Home Techniques in the Household Sector (

Conference papers

  • Reduction of Energy Consumption Using Smart Home Techniques in the Household Sector (