Performance of a photovoltaic solar shading system on an institutional building window

a multi-objective evolutionary analysis

Abstract

The climate crisis has led to an increasing use of renewable energy sources in buildings. Integrating photovoltaic systems into shading devices provides an effective strategy to reduce energy consumption while generating electricity. This study applies multi-objective optimization to identify efficient configurations of vertical and horizontal photovoltaic louvers, considering both energy production and consumption on the northeast façade of a computer lab in Pelotas (RS), Brazil. The research was structured in four stages, involving EnergyPlus simulations for vertical (1.38 kWp) and horizontal (2 kWp) photovoltaic louvers, along with multi-objective optimization using the NSGA-II algorithm across various scenarios. In these scenarios, the angles and spacing between elements were varied to enhance overall energy performance. The main contribution of this work to the state of the art, beyond its quantitative results, lies in the proposed method, which adopts an open-source approach implemented in Python and employs trigonometric equations to define the movement of the louvers. The best production-to-consumption ratio was achieved through the optimization of horizontal louvers with varying angles and wider spacing, resulting in energy generation exceeding consumption (108.20%) and a 5.76% reduction in electricity use.