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9 articles for “Meteorological Variables”
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Very Short-Term Load Forecasting Using Gaussian Process Regression
Abstract: Very Short-Term Load Forecasting (VSTLF) is critical for real-time grid stability, frequency control, and economic dispatch. This study proposes a Gaussian Process Regression (GPR)-based framework for one-hour-ahead load forecasting using hourly data from January 2020 to April 2024 for Delhi, India. The model incorporates meteorological data such as temperature, humidity, and dew point with lagged load values. The research takes into account time-related dependencies and seasonal changes in order to …
Published in Trends in Electrical Engineering · Vol. 16, Issue 1, 2025 · pp. 91–104 Read article
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Renewable Solar Energy Prediction in India : 2025-2030
Abstract: As India endeavors to realize its objective of achieving 500 GW of renewable energy capacity by the year 2030, the precise forecasting of solar power generation is rendered increasingly essential. This scholarly article conducts a comprehensive review of the utilization of machine learning (ML) methodologies in the prediction of solar energy across diverse Indian states, underscoring their potential to address the complexities associated with the variability of solar irradiance. Conventional …
Published in Journal of Power Electronics and Power Systems · Vol. 14, Issue 3, 2024 · pp. 41–46 Read article
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Atmospheric Boundary Layer Processes: Impacts on Weather Patterns and Climate Change
Abstract: The atmospheric boundary layer (ABL) is a vital component of the Earth's climate system, acting as the interface between the terrestrial surface and the overlying atmosphere. This layer, typically extending from the surface to a height of a few hundred meters, is characterized by strong gradients in meteorological variables such as temperature, humidity, wind speed, and atmospheric pressure. Understanding the dynamics of the ABL is essential for comprehending weather phenomena, …
Published in International Journal of Atmosphere · Vol. 1, Issue 2, 2024 · pp. 22–25 Read article
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AI/ML-Based Approach to Solar Irradiance Prediction and Energy Suitability
Abstract: In this paper, due to challenges in precisely predicting solar irradiance, which is essential for solar power system optimization, we employed six diverse machine learning (ML) techniques: Linear Regression, Decision Tree, Random Forest, Gradient Boosting methods (including XGBoost), and Neural Networks—to analyze and predict outcomes using a dataset containing meteorological and temporal features. Key variables include wind speed, humidity, and temperature, which significantly influence the model’s predictive capability. Each method …
Published in Journal of Alternate Energy Sources & Technologies · Vol. 16, Issue 3, 2025 · pp. 36–48 Read article
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Tropospheric Variation of Point Refractivity Gradient and Geo-climatic Factor over a Coastal Location in Tropical Nigeria
Abstract: Accurate assessment of refractivity gradient and Geo-climatic factor is crucial for maintaining reliable radio signal propagation in clear-air environments. These parameters are essential for determining the fade margin necessary to ensure a stable and effective wireless radio link, given the unstable nature of the atmosphere through which the signals travel. It is crucial to have accurate knowledge of these characteristics, particularly at microwave antenna heights of around 70 meters, in …
Published in Journal of Remote Sensing & GIS · Vol. 15, Issue 2, 2024 · pp. 28–42 Read article
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Time Series Forecasting Based on PyAF and fbProphet
Abstract: Time series forecasting is the technique of predicting future events using previous data. Time series data includes information that is collected and recorded at regular intervals, such as daily stock prices, monthly sales figures, or hourly temperature readings. The purpose of time series forecasting is to use previous data to create accurate forecasts about the future values of a given variable. This can be beneficial for a range of applications, …
Published in International Journal of Information Security Engineering · Vol. 1, Issue 1, 2023 · pp. 32–36 Read article
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Time Series Methods in Meteorology: A Review of Predictive Models and Applications
Abstract: The accurate prediction of time series data holds substantial significance in various fields, enabling informed decision-making and resource optimization. In this study, temperature variations over time are predicted using the Autoregressive Integrated Moving Average (ARIMA) model. Reliable temperature projections are more important now than ever because of climate change and its effects. For time series prediction problems, the ARIMA model—which is well-known for its ability to capture temporal dependencies in …
Published in Research & Reviews : Journal of Space Science & Technology · Vol. 13, Issue 2, 2024 · pp. 35–46 Read article
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Integrating Atmospheric Science: Understanding Greenhouse Gases, Aerosols, and Air Quality Dynamics
Abstract: Atmospheric science investigates the Earth’s atmospheric systems to understand their composition, dynamics, and the implications for climate, weather, and air quality. This review explores five primary areas within the field: atmospheric composition, atmospheric modeling, remote sensing, air pollution, and boundary layer dynamics, highlighting critical challenges and advancements. Rising levels of greenhouse gases (GHGs), including carbon dioxide and methane, continue to drive global warming, while feedback mechanisms—like cloud interactions and surface …
Published in International Journal of Atmosphere · Vol. 1, Issue 1, 2024 · pp. 32–35 Read article
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Climate Change and Air Pollution Dynamics: Synergistic Effects and Mitigation Strategies
Abstract: Climate change and air pollution are deeply interlinked environmental problems that jointly exacerbate human health, ecosystem integrity, and economic well‑being. As global temperatures rise, shifts in meteorological conditions—such as increased heat, altered precipitation, and more frequent extreme weather events—modify pollutant generation, dispersion, chemical transformation, and removal processes. Meanwhile, many sources of air pollution are also sources of greenhouse gases (GHGs), giving rise to potential co‑benefits or trade‑offs when formulating mitigation …
Published in International Journal of Environmental Noise and Pollution Control · Vol. 3, Issue 2, 2025 · pp. 38–42 Read article