# Exploring Local Explanation of Practical Industrial AI Applications: A Systematic Literature Review
## 3 Survey Results on Local Explanation in Industrial AI Applications
### Qualitative Analysis
#### 3.2.2 Energy and Building Management  
As presented in Table 2, our survey results show that XAI local explanation research has concentrated on time-series and tabular data with energy and building management applications.  
Much research has used an agnostic and post hoc design for time-series data and two main approaches, SHAP and LIME. For example, [PERSON] et al. [45] proposed an approach that improves the current Energy Star calculation method and employs extra model output processing to illuminate how a building can reach a particular score. [PERSON] et al. [57] used SHAP to analyze and interpret the XGBoost-based grid search power load forecasting model. While [PERSON] et al. [52] presented an AI-assisted control of district heating systems (DHS) for effective heat distribution. [PERSON] et al.'s explanation of the energy demand forecast model employing feature importance and attention methods was published in [44]. To support and elucidate the forecasts, [PERSON] et al. [56] suggested a strategy based on the LIME method. Both [PERSON] et al. [59] and [PERSON] et al. [58] used distinct and ante hoc XAI techniques with feature-important analysis. While [PERSON] et al. proposed the QLattice for estimating the final energy performance of residential buildings, [PERSON] et al. evaluated the most crucial aspects of electrical load forecasting. However, several of these studies' weaknesses must be addressed in future research. For example, the generalizability of the procedures and outcomes only relates to the input data tested. The explanations may also be difficult for average consumers to comprehend [45]. Future studies could concentrate on developing technical research and apps to provide more intuitive explanations to general consumers [57]. Furthermore, robust model interpretability can sometimes be difficult, and future studies should investigate alternate brain models and ways of explaining black-box models [59].  
The application of various XAI techniques to tabular data, with an agnostic and post hoc approach, has been explored. Specifically, LIME and SHAP have been investigated for generating local explanations. Examples include [PERSON]'s SHAP-based XAI-FDD [61], [PERSON]'s SHAP-based analysis of input variables for energy consumption forecasting [63], and [PERSON]'s LIME and SHAP-based visualizations for personalized feedback on electricity consumption time-series data [62]. [PERSON] et al. [61] proposed XAI-FDD (explainable artificial intelligence-fault detection and diagnosis), which uses explanations generated for each data instance to detect incipient faults. By combining human expertise with the explanations provided by the XAI system, this approach could potentially improve accuracy in classification. The XAI-FDD may examine air-handling systems, renewable energy sources, and other building energy components. [PERSON] et al. [63] used SHAP-based XAI to examine how input variables affect energy use forecasting. Their research divided the input variables into three categories--strong, ambiguous, and weak--providing insights into which variables had the most significant impact. However, they only analyzed certain targets, input factors, and predictive models.  
Future studies may need to consider a more comprehensive range of socioeconomic variables for different types of buildings. [PERSON] et al. [62] developed five visualizations using ML and XAI methods, to provide personalized feedback on electricity consumption time-series data, incorporating domain-specific knowledge. However, their approach only considered SHAP-based XAI visualizations, and future research could explore other XAI methods, such as LIME, for additional visualizations.