## 4 Smart Grid
### Introduction
Therefore, it is crucial to see a higher resolution of energy sensing being performed within a building. For instance, this can be through sub-metering or by installing individual energy-sensing nodes at power sockets and individual distribution boards to get a sense of the actual activity being performed in every part of the building. Such an approach, if combined with AI, can serve many use cases in smart cities, such as occupancy monitoring, activity monitoring for the elderly, and of course, maintaining carbon-efficient operation within the building. However, with the current technologies, there are several challenges that are further highlighted in this section. * Data logging rate: This is one of the most important parameters and KPIs in any communication system. The first two KPIs deal with the data collection by ensuring that meaningful data are being gathered. However, the instant availability and accessibility of these data is crucial for applications that are time critical. As briefly mentioned earlier, high resolution energy data can serve multiple applications, such as occupancy and activity monitoring, which consequently can help in cases such as emergency evacuation, hence the need for the instant logging of data and rapid processing to avoid catastrophic consequences resulting from any delays in data transmission. Having introduced the KPIs of energy monitoring systems, it is crucial to see how this is applied in real-life, whilst highlighting the challenges and the opportunities for improvement. The following subsection presents a use case that aimed at promoting energy-conscious behaviour through the use of persuasive technology in the form of feedback on energy consumption in an NHS hospital in the U.K. [66]. The use case presented describes the developed system, technical specifications, achieved results, challenges and limitations. The latter points are then expanded upon to highlight how 6G can fill the gaps to yield  
Figure 8: Key performance indicators identified for smart energy monitoring systems. a more robust energy-saving system. Figure 9 shows the past and futuristic visions of energy-saving systems that address energy usage behaviour in the building sector. Promoting Energy-Conscious Behaviour Using Persuasive Technology: A National Health Service (NHS) Use Case  
This use case was an initiative aimed at utilising persuasive technology [78] to promote energy-conscious behaviour in a National Health Service (NHS) hospital, located in Medway towns, the United Kingdom. In [66], the authors present a socio-technical model that brings together a wireless electricity data logger (WEDL) and a human-computer interaction (HCI)-based dashboard to influence the behavioural change amongst members of staff working in hospital wards as depicted in the top right corner of Figure 9.  
The reliability of the initiative taken in the NHS hospital to reduce energy consumption is heavily reliant on that of the WEDL, which utilises the General Packet Radio Service (GPRS) technology to communicate the electricity consumption data (see Figure 10a,b). Figure 10: Smart electricity monitoring system installed in Medway NHS hospital. (a) Block diagram and data flow. (b) System installation. Figure 9: Smart utility monitoring for energy-saving systems—the past and the future. Upon the introduction of the WEDL system, a five-month intervention was designed and carried out, which involved raising the energy users' awareness of their consumption in the workplace, using data generated by the WEDL. The intervention resulted in a 10.5% reduction in electricity consumption, despite a rise of 32.3% in the number of patients. The comparison was made between the baseline data and the intervention data based on time of year analysis, meaning both sets of data were collected during the same months of the years 2018 (baseline--before the WEDL) and 2019 (intervention--after the WEDL) (see Figure 11).