Energy-efficient solar heating using a genetic algorithm

Abstract

Relevance: the growth of global energy consumption, rising electricity tariffs, and increasingly stringent environmental requirements stimulate the search for sustainable and energy-efficient solutions for building heating. The residential sector accounts for a significant share of total energy consumption, especially in regions with cold winters where the main load falls on heating. Traditional electric boilers, despite their simplicity, exhibit high dependence on the power grid and contribute substantially to nighttime peak loads. In this regard, improving the energy efficiency and autonomy of heating systems becomes an urgent scientific and engineering challenge.

Aim: to develop, mathematically describe, and simulate a hybrid residential building heating system (PV–TES–BESS) equipped with a GA-based operational scheduler, targeting Tashkent winter conditions, with the aim of minimizing daily grid energy consumption while maintaining T_in ≥ 20°C

Methods: international experience and comparative analysis of practical implementations of these control methods are utilized.

Results: modeling results for winter conditions in Tashkent show that the proposed system significantly reduces grid dependence and increases the autonomy of heating. The developed approach can serve as a basis for creating intelligent solar heating systems effective in cold climate regions.