Machine Learning Approach on Time Series for PV-Solar Energy

Sivakumar, S.; Neeraja, B.; Rani, M. Jamuna; Anandaram, Harishchander; Ramya, S.; Padhan, Girish; Saravanakumar, G.

doi:10.1155/2022/6458377

Cited by 6 publications

(1 citation statement)

References 27 publications

(31 reference statements)

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“…Sivakumar et al used ANN and regression modeling to develop a time series model. In those models, machine learning can help forecast solar energy output [14]. Otherwise, the authors combined LSTM with CNN, wavelet packet decomposition (WPD), wavelet transform (WT), and other methods, and combined the particle swarm algorithm (PSO) with the adaptive neuro-fuzzy inference system (ANFIS) to improve the performance, stability, and reliability of model extraction data features [15][16][17][18].…”

Section: Related Workmentioning

confidence: 99%

A Power Forecasting Method for Ultra-Short-Term Photovoltaic Power Generation Using Transformer Model

Tian

Fan

Wang

et al. 2022

Mathematical Problems in Engineering

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The volatility of solar energy, geographic location, and weather factors continues to affect the stability of photovoltaic power generation, reliable and accurate photovoltaic power prediction methods not only effectively reduce the operating cost of the photovoltaic system but also provide reliable data support for the energy scheduling of the light storage microgrid, improve the stability of the photovoltaic system, and provide important help for the optimization operation of the photovoltaic system. Therefore, it is an important study to find reliable photovoltaic power prediction methods. In recent years, researchers have improved the accuracy of photovoltaic power generation forecasting by using deep learning models. Compared with the traditional neural network, the Transformer model can better learn the relationship between weather features and has good stability and applicability. Therefore, in this paper, the transformer model is used for predicting ultra-short-term photovoltaic power generation, and the photovoltaic power generation data and weather data in Hebei are selected. In the experiment, the prediction result of the transformer model was compared to the GRU and DNN models to show that the transformer model has better predictive ability and stability. Experimental results demonstrated that the proposed Transformer model outperforms the GRU model and DNN model by a difference of about 0.04 kW and 0.047 kW in the MSE value, and 22.0% and 29.1% of the MAPE error. In addition, the public DC competition dataset is selected for control experiments to demonstrate the general applicability of the transformer model for PV power prediction in different regions.

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Section: Related Workmentioning

confidence: 99%

A Power Forecasting Method for Ultra-Short-Term Photovoltaic Power Generation Using Transformer Model

Tian

Fan

Wang

et al. 2022

Mathematical Problems in Engineering

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Machine learning and the renewable energy revolution: Exploring solar and wind energy solutions for a sustainable future including innovations in energy storage

Bin Abu Sofian,

Lim,

Siti Halimatul Munawaroh

et al. 2024

Sustainable Development

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This article evaluates the present global condition of solar and wind energy adoption and explores their benefits and limitations in meeting energy needs. It examines the historical and evolutionary growth of solar and wind energy, global trends in the usage of renewable energy, and upcoming technologies, including floating solar and vertical‐axis wind turbines. The importance of smart grid technology and energy storage alternatives for enhancing the effectiveness and dependability of renewable energy is explored. In addition, the role of Electric Vehicles (EVs) in a modern smart grid has been assessed. Furthermore, the economic benefits, and most recent technological developments of solar and wind energy and their environmental and social ramifications. The potential of solar and wind energy to meet the increasing global energy demand and the problems and opportunities facing the renewable energy industry have shown excellent promise. Machine learning applications for solar and wind energy generation are vital for sustainable energy production. Machine learning can help in design, optimization, cost reduction, and, most importantly, in improving the efficacy of solar and wind energy, including advancing energy storage. This assessment is a crucial resource for policymakers, industry leaders, and researchers who aim to make the world cleaner and more sustainable. Ultimately, this review has shown the great potential of solar and wind energy in meeting global energy demands and sustainable goals.

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