An Approach to Multivariable Regression Analysis of a Rooftop PV System in Households’ Sector in Tirana. A Novel Approach using Energy Modeling Tool

Abstract

Solar power prediction plays an essential role in functioning, mapping, and obtaining energy and climate goals in 2030 and beyond and contributing to real-time balancing of the power system. On the other side, electricity consumption is influenced by Heating Degree Days (HDD), Cooling Degree Days (CDD), average monthly temperature, energy management, human behaviour, architecture, orientation, and many other factors. The simulations are performed based on three consecutive energy consumption data for a typical dwelling in Tirana city. Once the base case scenario is designed, the model is validated based on the monthly electricity bills. In the base case scenario are included some energy efficiency measures (EEM) afterward the optimization of the supply side using PV and Solar Water Heating is part of our simulation, too. The focus of our work is to design a correlation between the relationship between consumption and generation of electricity given as a dependent variable (Y), which is regressed with weather parameters given as independent variables (Xi) under Durbin Watson statistics. The output of the study can help designers to compile a reliable power system, better utilization of energy resources, and forecasting accuracy analysis from both sides of the energy system (demand and supply side). The tested household and EEM applied in the proposed scenario may lead to an electricity reduction level of 8655 kWh per year and 76.6 % of solar fraction is used to meet the hot water demand.