Evolutionary and Aggressive Sampling for Pattern Revelation and Precognition in Building Energy Managing System with Nature-Based Methods for Energy Optimization

Abstract

This chapter presents a discussion on an alternative attempt to manage the grids that are in intelligent buildings such as central heating, heat recovery ventilation or air conditioning for energy cost minimization . It includes a review and explanation of the existing methodology and smart management system . A suggested matrix-like grid that includes methods for achieving the expected minimization goals is also presented. Common techniques are limited to central management using fuzzy-logic drivers, but referred redefining of the model is used to achieve the best possible solution with a surplus of extra energy. Ordinary grids do not permit significant development in the present state. A modified structure enhanced with a matrix-like grid is one way to eliminate basic faults of ordinary grids model, but such an intricate grid can result in sub-optimal resource usage and excessive costs. The expected solution is a challenge for different Ant Colony Optimization (ACO) techniques with an evolutionary or aggressive approach taken into consideration. Different opportunities create many latent patterns to recover, evaluate and rate. Increasing building structure can surpass a point of complexity, which would limit the creation of an optimal grid pattern in real time using the conventional methods. It is extremely important to formulate more aggressive ways to find an approximation of the optimal pattern within an acceptable time frame.