Conditional Probability-Based Soft Computing Techniques for Smart Energy Management Systems

In this era of modern times, considering energy demand, energy systems have grown in such complexity and are now more intelligently modeled to be able to deal with uncertainty, nonlinearity, and different contextual aspects that influence energy consumption. This research introduces a hybrid soft computing framework that complements fuzzy logic modeling with conditional probability estimation in order to improve prediction accuracies and decision-making processes for smart energy management systems (SEMS). Examples of input features include time of the day, ambient temperature, and occupancy level, which are preprocessed, normalized, and selected based on mutual information criteria to maximize relevance. Fuzzy inference rules are developed based on expert knowledge and produce triangular membership functions from which interpretable reasoning is possible under vagueness conditions. Alongside this, conditional probabilities are inferred using empirical distributions for the assessment of uncertainty based on context. Validation of the model is by several evaluation measures, including MAE, RMSE, MAPE, R2, as well as visual diagnostics and conditioned statistical assessment. Lastly, an optimization layer is brought in to achieve energy cost minimization under operational constraints through predictive outputs and their confidence intervals. The findings indicate the robustness of the model for real-time deployment through SEMS.