ARTIFICIAL INTELLIGENCE IN PROBLEMS OF ENERGY PERFORMANCE OPTIMIZATION OF SMART CITIES

Abstract

The future of energy lies in energy self-sufficiency, renewable electricity generation, and digital technologies. The main focus is on optimizing the generation and consumption of electric power. Decentralized energy, optimization and regulation of generation and consumption of electricity to the level of the smallest producers and consumers - this is a power industry of the future. This is closely related to the solution of optimization of organisation of the electric power sources mainly from renewable energy sources (RES) in the system of their composition in the so-called RES microgrid as a decentralized power industry. A computer program will be designed to address the planning of local RES source organisation and their supply with electric power from the RES microgrid of a fictitious smart urban area. The purpose of the solution is stable energy balance in order to minimize the total cost of electricity generation, which is, in this case, determined by the prediction of its consumption in a specified period with sampling every hour. A program and a breakdown of organisation of electric power sources and their outputs covering the predicted consumption will be created. A prerequisite for the optimization or more precisely multicriterial optimization is development of special-purpose (cost, critical) function with the simultaneous definition of an optimality criterion such as penalty and formulation of restrictive conditions. Through the experiment, the conclusion of an efficient solution to the problem of optimization of electric power source organisation will be proven. The proposed optimization problem should be solved by a stochastic algorithm – simulated annealing, which is the most efficient algorithm for solution of our problem optimization. At the end, the procedures of the optimization problem solution for the special-purpose function of the RES microgrid energy system when supplying smart cities with electric power will be evaluated. The recorded annual history of electric power consumption can be sorted by day and by hour, and, then, we can see it as multidimensional data.
Identification of so-called typical daily consumption patterns can be compared to a cluster analysis of the multidimensional data, where the type day charts are formed by prototypes of identified clusters correlated with a certain season. The modelled yearly history of energy consumption and the use of cluster analysis, including the use of so-called self-organizing neural network and, through the Kohenen map, the required type day charts of the relevant period have been obtained.
In our experiment published hereto, we will come out of the historical electric power consumption data for working day – Wednesday. We will not address the issue of type day charts in this article; this is another very important independent scientific contribution.