A user-friendly and accurate machine learning tool for the evaluation of the worldwide yearly photovoltaic electricity production - Research paper

Aug 5, 2026

Upload by Finn

Machine learning is applied to overcome the time, expertise, and tooling burdens of traditional analytical and empirical modelling of photovoltaic (PV) thermal-electric behaviour. The study proposes a data-driven artificial neural network (ANN) tool that forecasts yearly PV electricity directly at the optimal PV inclination angle without geographic restrictions, supported by developed empirical correlations for inclination selection. The ANN is trained in Matlab using yearly climatic inputs and PV electrical/thermal parameters, with yearly electricity obtained via TRNSYS dynamic simulations across 48 worldwide climates for multiple PV modules, achieving high accuracy and validated robustness.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages28

Reading Time1 h 11 min

AccessFree to download

Introduction

Context

Article summary

Proposed ANN-based forecasting tool

Optimal PV inclination correlations

Model inputs, simulation method, and validation

Keywords

| Research paper
A user-friendly and accurate machine learning tool for the evaluation of the worldwide yearly photovoltaic electricity production | | | | --- | --- | --- | | Domenico Mazzeo a ,∗, Sonia Leva a , Nicoletta Materab , Karolos J. Kontoleon c , Shaik Saboord , Behrouz Pirouzb , Mohamed R. Elkadeem e,f
a Department of Energy, Politecnico di Milano, IT-20156, Milan, Italy
b Independent Researcher, IT-87036, Rende (CS), Italy
c Department of Civil Engineering, Aristotle University of Thessaloniki (A. U.Th. ), University Campus, GR-54124, Thessaloniki, Greece d School of Mechanical Engineering, Vellore Institute of Technology (VIT), IN-632014, Vellore, Tamil Nadu, India
e Electrical Power and Machines Engineering Department, Faculty of Engineering, Tanta University, EG-31521, Tanta, Egypt f Interdisciplinary Research Center for Renewable Energy and Power Systems (IRC-REPS), King Fahd University of Petroleum and Minerals (KFUPM), SA-31261, Dhahran, Saudi Arabia | | | | a r t i c l e i n f o | a b s t r a c t
While traditional methods for modelling the thermal and electrical behaviour of photovoltaic (PV) modules rely on analytical and empirical techniques, machine learning is gaining interest as a way to reduce the time, expertise, and tools required by designers or experts while maintaining high accuracy and reliability. This research presents a data-driven machine learning tool based on artificial neural networks (ANNs) that can forecast yearly PV electricity directly at the optimal PV inclination angle without geographic restrictions and is valid for a wide range of electrical characteristics of PV modules. Additionally, empirical correlations were developed to easily determine the optimal PV inclination angle worldwide. The ANN algorithm, developed in Matlab, systematically and quantitatively summarizes the behaviour of eight PV modules in 48 worldwide climatic conditions. The algorithm’s applicability and robustness were proven by considering two different PV modules in the same 48 locations. Yearly climatic variables and electrical/thermal PV module parameters serve as input training data. The yearly PV electricity is derived using dynamic simulations in the TRNSYS environment, which is a simulation program primarily and extensively used in the fields of renewable energy engineering and building simulation for passive as well as active solar design. Multiple performance metrics validate that the ANN-based machine learning tool demonstrates high reliability and accuracy in the PV energy production forecasting for all weather conditions and PV module characteristics. In particular, by using 20 neurons, the highest value of R-square of 0.9797 and the lowest values of the root mean square error and coefficient of variance of 14.67 kWh and 3.8%, respectively, were obtained in the training phase. This high accuracy was confirmed in the ANN validation phase considering other PV modules. An R-square of 0.9218 and values of the root mean square error and coefficient of variance of 31.95 kWh and 7.8%, respectively, were obtained.
The results demonstrate the algorithm’s vast potential to enhance the worldwide diffusion and economic growth of solar energy, aligned with the seventh sustainable development goal.
© 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license ([http://creativecommons.org/licenses/by-nc-nd/4.0/](http://creativecommons.org/licenses/by-nc-nd/4.0/)). | | | Article history:
Received 17 January 2023
Received in revised form 26 April 2023 Accepted 22 May 2023
Available online xxxx | | | | Keywords:
Photovoltaic module Electricity
Machine learning Artificial neural network PV forecasting Validation | | | 1. Introduction 1.1. Context Clean or renewable energies are those that can be generated simultaneously with their consumption. In contrast to nonrenewable sources such as oil, coal, and gas, renewable ene

FAQs

What does the proposed machine learning tool predict for photovoltaic systems?

It forecasts yearly PV electricity at the optimal PV inclination angle without geographic restrictions.

How are the neural network inputs and target values generated?

Yearly climatic variables plus PV electrical/thermal parameters are used as training inputs, and yearly PV electricity is derived from dynamic simulations in TRNSYS.

How was the ANN tool validated and what accuracy was reported?

Applicability and robustness were tested across two PV modules in the same 48 locations, using multiple performance metrics. The training phase reported a high R-square and low error, and validation produced similarly reliable results for other modules.

Aug 5, 2026

Upload by Finn