Approach towards Weather Prediction Model for Aggrotech

Sharma, Garima; Dubey, Sanjay Kumar

doi:10.1109/icais56108.2023.10073904

2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS) 2023

DOI: 10.1109/icais56108.2023.10073904

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Approach towards Weather Prediction Model for Aggrotech

Garima Sharma

Sanjay Kumar Dubey

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Cited by 3 publications

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Multivariate temperature prediction model based on CNN-BiLSTM and RandomForest

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Zhang,

Feng

et al. 2024

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Multivariate temperature prediction model based on CNN-BiLSTM and RandomForest

Bai,

Zhang,

Feng

et al. 2024

J Supercomput

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An Empirical Analysis of Renewable Energy Grid Connected Supply: A Review

Khan,

Ahmad,

Kundu

2025

CMS

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A grid is an interconnected network for electricity delivery from the producer to the consumer. This review focuses on the challenges associated with the integration of renewable energy into the grid, as well as the solutions that can be used to reduce the environmental impact of climate change and achieve sustainability. Renewable energy such as solar energy, wind energy, hydroelectricity, geothermal energy, and tidal energy are the sources which can help us to achieve the reduction of CO2 emission in the atmosphere, which is directly linked to climate change and global warming. In our study, we focussed on sustainable development and decreased CO2 emissions. The grid is essentially an interconnected network for delivering electricity from the manufacturer to the consumer, and the manufacturing segment should be replaced by renewable energy for us to meet our goal.

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Classification of Weather Conditions Based on Supervised Learning for Swedish Cities

Safia,

Abbas,

Aslani

2023

Atmosphere

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Weather forecasting has always been challenging due to the atmosphere’s complex and dynamic nature. Weather conditions such as rain, clouds, clear skies, and sunniness are influenced by several factors, including temperature, pressure, humidity, wind speed, and direction. Physical and complex models are currently used to determine weather conditions, but they have their limitations, particularly in terms of computing time. In recent years, supervised machine learning methods have shown great potential in predicting weather events accurately. These methods use historical weather data to train a model, which can then be used to predict future weather conditions. This study enhances weather forecasting by employing four supervised machine learning techniques—artificial neural networks (ANNs), support vector machines (SVMs), random forest (RF), and k-nearest neighbors (KNN)—on three distinct datasets obtained from the Weatherstack database. These datasets, with varying temporal spans and uncertainty levels in their input features, are used to train and evaluate the methods. The results show that the ANN has superior performance across all datasets. Furthermore, when compared to Weatherstack’s weather prediction model, all methods demonstrate significant improvements. Interestingly, our models show variance in performance across different datasets, particularly those with predicted rather than observed input features, underscoring the complexities of handling data uncertainty. The study provides valuable insights into the use of supervised machine learning techniques for weather forecasting and contributes to the development of more precise prediction models.

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Approach towards Weather Prediction Model for Aggrotech

Cited by 3 publications

References 20 publications

Multivariate temperature prediction model based on CNN-BiLSTM and RandomForest

Multivariate temperature prediction model based on CNN-BiLSTM and RandomForest

An Empirical Analysis of Renewable Energy Grid Connected Supply: A Review

Classification of Weather Conditions Based on Supervised Learning for Swedish Cities

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