## 4 Smart Grid  
### Introduction  
The race against climate change has been a great challenge for years and the U.K. government has taken serious steps towards carbon neutrality. One of the primary focuses of the U.K. government, after becoming the first of the major economic leaders in the world to pass laws that put a stop to its contributions to climate change, by 2050 [59] is to expand the use of smart utility monitoring and improve energy-conscious behaviour. Extensive research has gone into promoting energy-conscious behaviour by providing feedback on energy consumption through various utility monitoring techniques [60], in university residential buildings [61; 62], private homes [63; 64; 65] and hospitals [66]. The focus is on how to utilise different forms of smart metering to promote energy-conscious behaviour through behavioural change interventions, which have shown the potential to provide immediate energy savings of up to 12% [67]. Nevertheless, there is more to smart utility monitoring than raising occupants' awareness--it is also used as means of collecting high-resolution energy data [68] to build forecasting models [69; 70]. Such models are useful for various reasons, including ensuring effective future energy supply, load management, and lastly energy management. This shows the great interest in high resolution energy data and the role it can play to reduce overall consumption in the building sector. The use of wireless communication and the IoT is inevitable in order to reliably collect high resolution energy data to enable the development of energy-saving systems. The rollout of smart metering started in 2011 in the U.K. [71] with several research studies published since then, discussing IoT-based energy monitoring and management systems [72; 73; 74; 75; 76]. The idea of a smart metering system is to make automatically available information about energy consumption in a centralised database. On a commercial level, such information is used by energy suppliers for billing energy users and predicting future consumption to adjust monthly tariffs. On a research level, the collected data are of great value to initiatives, such as behavioural change interventions, demand response studies, load management, and so on. The key performance indicators (KPIs) of an energy monitoring system can be subdivided into three branches (see Figure 8). The first is the resolution of the data, that is, how often data are captured, for example, daily, hourly, half-hourly (HH), or in real-time. The second branch covers the loads reflected by the data, that is, aggregate or individualised loads. Finally, the third branch is the data logging rate, that is, how quickly the data are made available to the beneficiaries. The three KPIs identified in Figure 8 have a vital role to play in evolving IoT-based energy monitoring systems for smart grids and smart cities. Each KPI is briefly elaborated upon below:  
* Energy Data Resolution: The resolution is greatly dependant on the application and the use of such data. However, higher resolution data are greatly sought as they enable fine-grained monitoring of consumption and recognition of the transient spikes in power demand, which can be useful in energy modelling [77]. Moreover, the availability of real-time consumption can greatly support behavioural change interventions by performing time-series analysis of occupancy vs. consumption to identify where energy-conscious behaviour needs to be promoted. * Energy load resolution: Another key feature in the collected data is the load resolution, that is, what the collected data reflect in terms of load. For instance, taking a university building as an example, data from the main cable reflect the aggregate electricity consumption in the whole building, and whilst this is useful information, on a top-level, researchers and end-users are more interested in much higher resolutions. Energy sensing at the main supplies can only give an indication of the behaviour and efficiency of energy usage by users and equipment.