@misc{10481/93538,
year = {2024},
month = {6},
url = {https://hdl.handle.net/10481/93538},
abstract = {Time series forecasting still awaits a transformative breakthrough like that happened in
computer vision and natural language processing. The absence of extensive, domain-independent
benchmark datasets and standardized performance measurement units poses a significant
challenge for it, especially for photovoltaic forecasting applications. Additionally, since it is often
time domain-driven, a plethora of highly unique and domain-specific datasets were produced.
The lack of uniformity among published models, developed under diverse settings for varying
forecasting horizons, and assessed using non-standardized metrics, remains a significant obstacle
to the progress of the field as a whole. To address these issues, a systematic review of the state-ofthe-
art literature on prediction tasks is presented, collected from the Web of Science and Scopus
databases, published in 2022 and 2023, and filtered using keywords such as “photovoltaic,”
“deep learning,” “forecasting,” and “time series.” Finally, 36 case studies were selected. Before
comparing, a state-of-the-art demonstration of key elements in the topic was presented, such as
model type, hyperparameters, and evaluation metrics. Then, the 36 articles were compared in
terms of statistical analysis, including top publishing countries, data sources, variables, input,
and output horizon, followed by an overall model comparison demonstrating every proposed
model categorized into model type (artificial neural network units, recurrent units, convolutional
units, and transformer units). Due to the mostly utilization of specific private datasets measured
at the targeted location, having universal error metrics is crucial for clear global benchmarking.
Root Mean Squared Error and Mean Absolute Error were the most utilized metrics, although they
specifically demonstrate the accuracy relative to their respective sites. However, 33% utilized
universal metrics, such as Mean Absolute Percentage Error, Normalized Root Mean Squared
Error, and the Coefficient of Determination. Finally, trends, challenges, and future research were
highlighted for the relevant topic to spotlight and bypass the current challenges.},
publisher = {Elsevier},
keywords = {Photovoltaic power forecasting},
keywords = {Deep learning},
keywords = {Time series},
title = {Towards energy efficiency: A comprehensive review of deep learning-based photovoltaic power forecasting strategies},
doi = {10.1016/j.heliyon.2024.e33419},
author = {Mauladdawilah, Husein and Gago, E.J. and Balfaqih, Hasan and Pegalajar, M.C.},
}