# A Cyber-Physical Approach for Residential Energy Management: Current State and Future Directions
## 8 Challenges and Main Research Directions  
In a general perspective, the main challenges to be handled in HEMS are related to achieving energy efficiency and dealing with implementation costs. To this end, a standard approach which allows to attend every HEMS application based on the collected data, is still a source of discussion in the research community. According to the aforementioned stages, a series of challenges were identified.  
Considering Data Acquisition, the following challenges exist:  
* Access to smart meter measurements is still limited in some countries due to regulation and implementation issues.
* High-resolution data cannot be achieved with most commercial smart meters today with complexity in setup, data storage, and cost.
* Smart appliances usage has been limited due to the high market prices and interoperability issues of these devices.
* Sensors capable of measuring at high sampling rates are needed to satisfy large-scale implementation requirements of HEMS.  
In terms of Communication Network, the following challenges were identified:  
* Interference and wall penetration losses are the main challenges to be handled in smart homes.
* More flexibility is needed, which translates into taking advantage of the unused spectrum.
* There is a need for technology which connects the smart homes toward developing a smart city infrastructure, and allowing real-time operation of multiple applications. The 5G and 6G technologies are strong candidates. However, identifying the requirements for embracing these technologies at different levels (home or city) is still a subject of debate.
* Conventional wireless communication technologies, such as WiFi or Zigbee, are insufficient for communication range, energy consumption, and cost of most HEMS applications today.  
Regarding Data Analytics, the following issues were found:  
* Different requirements must be considered regarding data resolution, accuracy, real-time, and the number of devices to be covered.
* NILM methods have less precision and higher difficulty to their deployment in real-world scenarios compared to ILM. The latter, in contrast, offer more reliability at expenses of cost. Therefore, developing a hybrid solution is an attractive solution for load monitoring. However, it introduces several challenges that need to be attended.
* Appliance level can be very useful for HEMS since it allows to identify usage patterns of individual appliances. However, this task has received less attention from the research community. Building a unique model which forecasts the consumption of different appliances is still more complicated to achieve.  
* Although reinforcement learning and rule-based approaches have been proposed for scheduling and control mechanisms, a detailed comparison (through a sensitivity analysis and/or evaluation) of both cases is needed.
* Consumer privacy can hinder the deployment of Smart Grids and HEMS since energy data expose the common habits and routines of users. Therefore, secure access to authenticated parties must be provided through cybersecurity and encryption mechanisms.