Data Analytics of IoT Enabled Smart Energy Meter in Smart Cities

Abstract

In current energy production and distribution system, a smart energy meter has been a significant conceptual paradigm. There is a dire requirement to make energy usage more efficient and effective due to limited nonrenewable energy resources and renewable energies (REs) available at high cost. It creates a critical environment for future economic developments and social improvements such as smart cities. In recent years, numbers of smart meters are being installed in residential areas and other sites of smart cities. Smart meters are capable to provide numerous informative recordings of electricity consumption along with accurate processing of billing, Automated Meter Reading (AMR) data processing, detection of energy theft and early warning of blackouts, fast detection of turbulences in energy supply, real time pricing updates, and Demand Response (DR) system for energy saving and efficient usage of energy generated. To take full benefit of smart metering intelligence, numbers of technical issues are required to be addressed. The major concern is to work with very large volume of data. There is a need to develop efficient data fusion and integration techniques. Numerous big data integration and analytics engines are required, which can perform tasks such as outage management, asset management and fault detection especially in case of DR system, customer segmenting, load forecasting and targeting. Data analytic approaches transform volume of data into actionable information for consumers, utilities and authorities. Although numerous analytical algorithms are available, which can process huge volume of data, but many of these are not capable to complete task sufficiently. A few research procedures have been accounted for on investigating streaming data, but still needs a lot of work to be done in making these commercially reasonable. In this chapter, smart energy meters’ data analytics framework is proposed by employing latest data processing techniques/tools along with gamification approach for enhancing consumers’ engagement. Benefits of smart energy meter’s analytics are also discussed for motivating consumers, utilities and stakeholders. Researchers, utilities, authorities can take benefits from proposed algorithm by planning their future action with supplementary participation of real time consumers due to gamification approach. By gamification, consumers’ engagement improves and it alters their less sustainable behavior on a voluntary basis.