# Demand Flexibility: A Review of Quantification Methods, Models, and Required Data
## 4 Discussion
### Research Gaps and Future Directions
#### 4.2.3 Data Availability  
There are common types of data used by authors in this field. Many studies focus on a single or a limited number of end-uses for analysis, which narrows the data required to inform their methodology. For example, studies that analyzed A/C flexibility potential often used historical demand data, temperature data, building characteristics, and technology characteristics. Although these might be suitable for A/C-specific studies, they do not provide a complete picture of the building response to a DR event and the effects on occupants; increased diversity of data is required to account for all contributors to flexibility variations. An accurate demand flexibility model needs input data that are comprehensive and granular. As illustrated in Section 3.5, common datasets seen across studies include demand, weather/climate, building characteristics, appliance characteristics, and grid data. These all focus on the technical characteristics and factors driving flexibility, and as such, may not account for all variables contributing to flexibility potential estimations. For example, the authors of [45] identified various drivers of price responsiveness that are not reflected in the data evaluated in Section 3.5. Among other factors, they identified income status, the number of occupants, and perceived behavioral control as being contributors to price responsiveness. However, these factors were not seen frequently--if at all--in flexibility studies. References [12; 54] included a subset of those drivers but were not comprehensive. Determining which attributes contribute to flexibility potential may require direct engagement with customers and stakeholders to gain a deeper understanding of the drivers of flexibility and identify the data needed to enhance confidence in estimations. Another limitation involves the accessibility of data. While building characteristics, technology characteristics, and historical demand may generally be accessible, other useful (and at times necessary) data, such as indoor temperature data and occupancy data, are often unavailable due to privacy concerns, lack of granular measurements, or both. This can negatively impact the accuracy of the developed model by ignoring variables that have a direct impact on demand and its flexibility potential. The issue can be partially addressed by either estimating the unavailable data or including surrogate data. For instance, indoor temperatures can be estimated using physics-based models and occupancy can be estimated based on non-intrusive load monitoring methods. However, this approach can cause cumulative errors in the model. A more severe challenge occurs when modeling user behavioral patterns with respect to DR and demand flexibility. User behavior data cannot be easily measured or estimated. In fact, large-scale, longitudinal surveys might be the only viable option to collect meaningful data to develop models. But even those models may not be easily generalizable. An opportunity exists for multidisciplinary sociotechnical analyses spanning engineering, economics, and social sciences to develop models that reflect how users interact with energy systems and DR programs. Even when the required data are available, they may not have the necessary granularity levels. For instance, an accurate demand flexibility model of a building would need usage data from individual appliances. Such data are difficult to obtain for an individual building, but almost infeasible to collect for a cluster of buildings or a region. Advanced deep learning techniques, as described above, might be used to detect consumption patterns and signatures of various appliances from aggregate meter data. Another example is related to occupancy detection. While general occupancy can be determined easily from meter data, determining the number and age of individuals in the building, without direct measurement, is far more complicated. At the same time, the demand profile and response to DR events might be directly impacted by which occupants are inside the building.