Forecasting renewable energy generation with machine learning and deep learning: Current advances and future prospects

NE Benti, MD Chaka, AG Semie - Sustainability, 2023 - mdpi.com
This article presents a review of current advances and prospects in the field of forecasting
renewable energy generation using machine learning (ML) and deep learning (DL) …

Machine vision based fault diagnosis of photovoltaic modules using lazy learning approach

SN Venkatesh, V Sugumaran - Measurement, 2022 - Elsevier
Abstract Machine Vision is an advanced and powerful imaging based technique that has
been applied in various fields like robotics, inspection and process control. Machine vision …

[HTML][HTML] Automatic detection of visual faults on photovoltaic modules using deep ensemble learning network

SN Venkatesh, BR Jeyavadhanam, AMM Sizkouhi… - Energy Reports, 2022 - Elsevier
The present study proposes an ensemble-based deep neural network (DNN) model for
autonomous detection of visual faults such as glass breakage, burn marks, snail trail, and …

Convolutional neural network based automatic detection of visible faults in a photovoltaic module

NV Sridharan, V Sugumaran - Energy Sources, Part A: Recovery …, 2021 - Taylor & Francis
ABSTRACT Background/Objective: The primary objective of the present study is to
distinguish several visual faults which hinder the performance, reliability and lifetime of …

Visual fault detection in photovoltaic modules using decision tree algorithms with deep learning features

NV Sridharan, V Sugumaran - Energy Sources, Part A: Recovery …, 2021 - Taylor & Francis
Visual faults in photovoltaic (PV) modules persist as a problem that can create
consequences such as reduced life span, increased output power loss and raising safety …

A cross-sectional survey of deterministic PV power forecasting: Progress and limitations in current approaches

A Sabadus, R Blaga, SM Hategan, D Calinoiu… - Renewable Energy, 2024 - Elsevier
This review reports a quantitative analysis across the deterministic photovoltaic (PV) power
forecasting approaches. Model accuracy tests from papers passing a set of selection criteria …

What drives the accuracy of PV output forecasts?

TN Nguyen, F Müsgens - Applied Energy, 2022 - Elsevier
In this paper, 180 papers on photovoltaic (PV) output forecasting were reviewed and a
database of forecast errors was extracted for statistical analysis. The paper shows that …

Enhancing Photovoltaic Module Fault Diagnosis with Unmanned Aerial Vehicles and Deep Learning‐Based Image Analysis

J Jerome Vasanth, S Naveen Venkatesh… - International Journal …, 2023 - Wiley Online Library
Artificial intelligence (AI) has evolved into a powerful tool that has wide‐spread application
in computer vision such as computer‐aided inspection, industrial control systems, and …

Accurate photovoltaic power prediction models based on deep convolutional neural networks and gated recurrent units

NM Sabri, M El Hassouni - Energy Sources, Part A: Recovery …, 2022 - Taylor & Francis
Solar energy is a feasible alternative to traditional sources of energy. However, the
intermittent and random nature of photovoltaic power generation poses a challenge to the …

A comparative study on bayes classifier for detecting photovoltaic module visual faults using deep learning features

SN Venkatesh, V Sugumaran, B Subramanian… - Sustainable Energy …, 2024 - Elsevier
Renewable energy is found to be an effective alternative in the field of power production
owing to the recent energy crises. Among the available renewable energy sources, solar …