Impact of Internet of Things paradigm towards energy consumption prediction: a systematic literature review

• Main Authors: Cheng, Yew Leong, Lim, Meng Hee, Hui, Kar Hoou
• Format: Article
• Published: Elsevier Ltd 2022
• Subjects: QA75 Electronic computers. Computer science
• Online Access: http://eprints.utm.my/104524/; http://dx.doi.org/10.1016/j.scs.2021.103624

The contribution of buildings to energy consumption (both residential and commercial) is expected to gradually increase by 2040 in developed countries globally. Energy demand is rising around the world because of population growth and increased access to power through rapid urbanisation. These increases significantly impact the environment due to the processing of electricity from fossil fuels in heavy-duty power generation plants to cater for demand. A large amount of research has been undertaken in the recent past to mitigate energy usage via the internet of things and energy consumption prediction (IoT-ECP). However, systematic reviews of IoT-ECP applications remain scarce. Therefore, this study aims to systematically review the existing literature to identify the latest trends and their technological advances, including the integration concept and solutions to problems encountered in IoT-ECP. It also highlights the advantages between cloud and edge computing in IoT-ECP integration for real-time data streaming. Additionally, IoT-ECP smart integration promotes close interaction and monitoring of energy usage. Battery storage is a major challenge to tackle energy losses along network bandwidth and live streaming data traffic. Finally, the studies indicates that many existing research in IoT-ECP are using short-term load predictions. These are domains where future research could further expand to cover medium- to long-term time frames, forecasting to better balance demand fluctuations, and provide operational reserves with renewable energies.