Predicting daily household energy usages by using Model Agnostic Language for Exploration and Explanation

Since urbanization is occurring at an exponential rate today, energy saving is a key factor for the majority of sustainable smart cities. Out of that, the majority of energy usage is directed toward homes, where there is an enormous possibility for energy optimization. As a result, most academics believe that forecasting this household energy using the advent of AI and machine learning techniques will have social benefits. However, predicting energy consumption alone won't help a city optimize its utilization of energy; it's also crucial to comprehend the factors that influence such predictions so that any available countermeasures can be applied and the city can make decisions about energy optimization that are more accountable, trustworthy, and justifiable to all of its stakeholders. There are different categories of explainers that offer the ability to explore a black box model. Each of these explanations has a connection to a certain model feature. Here, dalex, a Python library that implements a type of explanation, is utilized. a model-neutral user interfaces for interactive fairness and interpretability. It can make machine learning models more understandable. This method is used in this case to know the prediction model and discover the factors responsible for household energy consumption together including their relative importance.