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A framework of using machine learning approaches for short-term solar power forecasting
Various machine learning approaches are widely applied for short-term solar power
forecasting, which is highly demanded for renewable energy integration and power system …
forecasting, which is highly demanded for renewable energy integration and power system …
Solar power forecast for a residential smart microgrid based on numerical weather predictions using artificial intelligence methods
Solar power forecast is a much needed means for grid operators, particularly in residential
microgrids, to manage the produced energy in a dispatchable fashion. Deterministic …
microgrids, to manage the produced energy in a dispatchable fashion. Deterministic …
Univariate time series prediction of solar power using a hybrid wavelet-ARMA-NARX prediction method
This paper proposes a new hybrid method for super short-term solar power prediction. Solar
output power usually has a complex, nonstationary, and nonlinear characteristic due to …
output power usually has a complex, nonstationary, and nonlinear characteristic due to …
[HTML][HTML] Enhancing solar power forecasting with machine learning using principal component analysis and diverse statistical indicators
Abstract _Predicting solar energy is essential for efficient power system planning and the
successful integration of renewable energy sources. This study aims to develop a framework …
successful integration of renewable energy sources. This study aims to develop a framework …
Diformer: A dynamic self-differential transformer for new energy power autoregressive prediction
Power prediction is important as a technical support mechanism in the global carbon
neutrality initiative. Existing artificial intelligence methodologies frequently grapple with the …
neutrality initiative. Existing artificial intelligence methodologies frequently grapple with the …
Forecasting of solar radiation for a cleaner environment using robust machine learning techniques
M Thangavelu, VJ Parthiban, D Kesavaraman… - … Science and Pollution …, 2023 - Springer
Intensified research is going on worldwide to increase renewable energy sources like solar
and wind to reduce emissions and achieve worldwide targets and also to address the …
and wind to reduce emissions and achieve worldwide targets and also to address the …
[HTML][HTML] Comparative study of univariate and multivariate long short-term memory for very short-term forecasting of global horizontal irradiance
Accurate global horizontal irradiance (GHI) forecasting is crucial for efficient management
and forecasting of the output power of photovoltaic power plants. However, develo** a …
and forecasting of the output power of photovoltaic power plants. However, develo** a …
Daily surface solar radiation prediction map** using artificial neural network: The case study of Reunion Island
This paper focuses on the prediction of daily surface solar radiation maps for Reunion Island
by a hybrid approach that combines principal component analysis (PCA), wavelet transform …
by a hybrid approach that combines principal component analysis (PCA), wavelet transform …
[PDF][PDF] Solar irradiance forecasting using fuzzy logic and multilinear regression approach: a case study of Punjab, India
The accurate forecasting of solar irradiance depends on various uncertain parameters like
time of day, temperature, wind speed, humidity, and atmospheric pressure. All these play an …
time of day, temperature, wind speed, humidity, and atmospheric pressure. All these play an …
Spatial predictions of solar irradiance for photovoltaic plants
I Jayawardene… - 2016 IEEE 43rd …, 2016 - ieeexplore.ieee.org
Today's electrical power grid is going through an important transition, where the integration
of clean and renewable energy sources is an essential fact. The increasing penetration of …
of clean and renewable energy sources is an essential fact. The increasing penetration of …