An evaluation of CNN and ANN in prediction weather forecasting: A review

Kareem, Shahab Wahhab; Hamad, Zhala Jameel; Askar, Shavan

doi:10.37868/sei.v3i2.id146

Cited by 13 publications

(7 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This level of performance, particularly when assessed in the context of wind speed forecasting, underscores the efficacy of the CNN architecture in capturing complex spatial and temporal patterns inherent in meteorological data. The superior performance of the CNN over the FCN model in our study corroborates the findings from the existing literature, where CNNs are often reported to outperform FCNs in tasks that require the analysis of intricate data patterns [33,34].…”

Section: Discussionsupporting

confidence: 90%

Deep Learning for Wind and Solar Energy Forecasting in Hydrogen Production

Nikulins,

Sudars,

Edelmers

et al. 2024

Energies

View full text Add to dashboard Cite

This research delineates a pivotal advancement in the domain of sustainable energy systems, with a focused emphasis on the integration of renewable energy sources—predominantly wind and solar power—into the hydrogen production paradigm. At the core of this scientific endeavor is the formulation and implementation of a deep-learning-based framework for short-term localized weather forecasting, specifically designed to enhance the efficiency of hydrogen production derived from renewable energy sources. The study presents a comprehensive evaluation of the efficacy of fully connected neural networks (FCNs) and convolutional neural networks (CNNs) within the realm of deep learning, aimed at refining the accuracy of renewable energy forecasts. These methodologies have demonstrated remarkable proficiency in navigating the inherent complexities and variabilities associated with renewable energy systems, thereby significantly improving the reliability and precision of predictions pertaining to energy output. The cornerstone of this investigation is the deployment of an artificial intelligence (AI)-driven weather forecasting system, which meticulously analyzes data procured from 25 distinct weather monitoring stations across Latvia. This system is specifically tailored to deliver short-term (1 h ahead) forecasts, employing a comprehensive sensor fusion approach to accurately predicting wind and solar power outputs. A major finding of this research is the achievement of a mean squared error (MSE) of 1.36 in the forecasting model, underscoring the potential of this approach in optimizing renewable energy utilization for hydrogen production. Furthermore, the paper elucidates the construction of the forecasting model, revealing that the integration of sensor fusion significantly enhances the model’s predictive capabilities by leveraging data from multiple sources to generate a more accurate and robust forecast. The entire codebase developed during this research endeavor has been made available on an open access GIT server.

show abstract

Section: Discussionsupporting

confidence: 90%

Deep Learning for Wind and Solar Energy Forecasting in Hydrogen Production

Nikulins,

Sudars,

Edelmers

et al. 2024

Energies

View full text Add to dashboard Cite

show abstract

“…The various ANN architectures for weather forecasting for a comparable geographic region were presented by Kareem et al [63]. Convolutional Neural Network (CNN) and several ANN architectures, such as LSTM and BPNN algorithms, were assessed in the study.…”

Section: Rainfall Predictionmentioning

confidence: 99%

Analysis of Machine Learning Algorithms for Prediction of Short-Term Rainfall Amounts Using Uganda’s Lake Victoria Basin Weather Dataset

Gahwera,

Eyobu,

Isaac

2024

IEEE Access

View full text Add to dashboard Cite

As a result of climate change, the difficulty in the prediction of short-term rainfall amounts has become a necessary area of research. The existing numerical weather prediction models have limitations in precipitation forecasting especially due to high computation requirements and are prone to errors. Precipitation amount prediction is challenging as it requires knowledge on a variety of environmental phenomena, such as temperature, humidity, wind direction, and more over a long period of time. In this study, we first of all present our Lake Victoria Basin weather dataset and then use it to conduct a rigorous analysis of machine learning algorithms to do short term rainfall prediction. The rigorous analysis includes algorithm optimizations to improve prediction performance. In particular, we intend to validate our weather dataset using various machine learning regression models which include Random Forest regression, Support vector regression, Neural Network regression, Least Absolute Shrinkage and Selection Operator regression, Gradient boosting regression, and Extreme Gradient boosting regression. The performance of the models was assessed using Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) performance metrics. The findings demonstrate that, in comparison to other algorithms, Extreme Gradient Boost Regression had the lowest MAE values of 0.006, 0.018, 0.005 for Lake Victoria basin weather data in Uganda, Kenya, and Tanzania respectively.INDEX TERMS Precipitation Amount, Weather Prediction, Data-driven Approaches, Short-term Forecasting.WRF model results can be automated and checked with

show abstract

“…Convolutional Neural Networks have similarities with the traditional ANN method, where there are neurons that will perform self-optimization through the learning process. The most striking difference between CNNs and ANNs is that CNNs are mainly used in the field of pattern recognition on an image [38], [39].…”

Section: B Convolutional Neural Networkmentioning

confidence: 99%

Grouping of Image Patterns Using Inceptionv3 For Face Shape Classification

Hidayat,

Astuti,

Yaqin

et al. 2023

JOIV : Int. J. Inform. Visualization

View full text Add to dashboard Cite

The human face is an extraordinary part where nearly everybody is not quite the same as each other. One perspective that should be visible plainly is the shape. Face shape grouping can be used for amusement, security, or excellence. One technique that can be utilized in picture grouping is the InceptionV3 model. InceptionV3 is the structure of the Convolutional Neural Network (CNN) created by Google, which can tackle picture examination and item discovery issues. This engineering is utilized to order face shapes into five classes: Round, Heart, Square, Oblong, and Oval. At that point, the Google Pictures dataset goes through the pre-handling stage, and the Shrewd Edge Identifier is applied to each picture. Hair turns into a commotion. Consider recognizing the side of the face because it does not make any difference what the hairdo resembles. What is important is the side of the face. When there is a dataset of elongated class and heart class with a comparable hairdo, InceptionV3 will identify the component and expect the two pieces of information to come from a similar class. The exchange learning strategy is done in preparation for the last Layer of ImageNet's InceptionV3 model. This strategy puts the high precision level with an exactness of 93% preparation and testing between 88% - 98%. InceptionV3 could arrange upwards of 692 from 747 datasets or around 92.65%. The most reduced information class is the heart class, where out of 150 information, InceptionV3 can characterize upwards of 130 information.

show abstract

An evaluation of CNN and ANN in prediction weather forecasting: A review

Cited by 13 publications

References 28 publications

Deep Learning for Wind and Solar Energy Forecasting in Hydrogen Production

Deep Learning for Wind and Solar Energy Forecasting in Hydrogen Production

Analysis of Machine Learning Algorithms for Prediction of Short-Term Rainfall Amounts Using Uganda’s Lake Victoria Basin Weather Dataset

Grouping of Image Patterns Using Inceptionv3 For Face Shape Classification

Contact Info

Product

Resources

About