Using Stacked Long Short Term Memory with Principal Component Analysis for Short Term Prediction of Solar Irradiance based on Weather Patterns

Guia, Justin D. de; Concepcion, Ronnie; Calinao, Hilario A.; Alejandrino, Jonnel; Dadios, Elmer P.; Sybingco, Edwin

doi:10.1109/tencon50793.2020.9293719

Cited by 25 publications

(8 citation statements)

References 12 publications

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“…A recent study by [135] utilized a new variant of LSTM known as stacked LTSM integrated with principal component analysis (PCA) for solar irradiance forecasting on a 6-month dataset retrieved from a weather station at Morong, the Philippines. Among the input features considered were humidity, station height, wind speed, station temperature, absolute pressure, illuminance, sea level pressure, and ambient temperature.…”

Section: Solar Irradiance Forecasting Model Based On the Lstm Algorithmmentioning

confidence: 99%

A Review on Neural Network Based Models for Short Term Solar Irradiance Forecasting

et al. 2023

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The accuracy of solar energy forecasting is critical for power system planning, management, and operation in the global electric energy grid. Therefore, it is crucial to ensure a constant and sustainable power supply to consumers. However, existing statistical and machine learning algorithms are not reliable for forecasting due to the sporadic nature of solar energy data. Several factors influence the performance of solar irradiance, such as forecasting horizon, weather classification, and performance evaluation metrics. Therefore, we provide a review paper on deep learning-based solar irradiance forecasting models. These models include Long Short-Term Memory (LTSM), Gated Recurrent Unit (GRU), Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), Generative Adversarial Networks (GAN), Attention Mechanism (AM), and other existing hybrid models. Based on our analysis, deep learning models perform better than conventional models in solar forecasting applications, especially in combination with some techniques that enhance the extraction of features. Furthermore, the use of data augmentation techniques to improve deep learning performance is useful, especially for deep networks. Thus, this paper is expected to provide a baseline analysis for future researchers to select the most appropriate approaches for photovoltaic power forecasting, wind power forecasting, and electricity consumption forecasting in the medium term and long term.

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Section: Solar Irradiance Forecasting Model Based On the Lstm Algorithmmentioning

confidence: 99%

A Review on Neural Network Based Models for Short Term Solar Irradiance Forecasting

et al. 2023

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“…In [24], the authors used an artificial neural network, CNN, bidirectional and stacked LSTM to predict solar irradiance values. The used parameters are humidity, station and ambient temperature, station altitude, sea level pressure, absolute pressure and wind speed.…”

Section: Related Workmentioning

confidence: 99%

“…23Convective available potential energy CAPE (180 mb) measures the air parcel's potential energy per kilogram of the air mass. High CAPE value means that atmosphere is unstable and would produce a strong updraft 24. Wind Direction (10 m above ground)…”

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confidence: 99%

Solar Radiation Forecasting by Pearson Correlation Using LSTM Neural Network and ANFIS Method: Application in the West-Central Jordan

et al. 2022

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Solar energy is one of the most important renewable energies, with many advantages over other sources. Many parameters affect the electricity generation from solar plants. This paper aims to study the influence of these parameters on predicting solar radiation and electric energy produced in the Salt-Jordan region (Middle East) using long short-term memory (LSTM) and Adaptive Network-based Fuzzy Inference System (ANFIS) models. The data relating to 24 meteorological parameters for nearly the past five years were downloaded from the MeteoBleu database. The results show that the influence of parameters on solar radiation varies according to the season. The forecasting using ANFIS provides better results when the parameter correlation with solar radiation is high (i.e., Pearson Correlation Coefficient PCC between 0.95 and 1). In comparison, the LSTM neural network shows better results when correlation is low (PCC in the range 0.5–0.8). The obtained RMSE varies from 0.04 to 0.8 depending on the season and used parameters; new meteorological parameters influencing solar radiation are also investigated.

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“…PV power and solar spectral irradiance are closely related, and solar spectral irradiance is usually affected by other meteorological factors, so the characteristics of PV power are different under different weather conditions. This means that the accuracy of PV power prediction depends not only on the historical load data, but also on weather factors [21,22]. Ref.…”

Section: Introductionmentioning

confidence: 99%

A Short-Term Photovoltaic Power Forecasting Method Combining a Deep Learning Model with Trend Feature Extraction and Feature Selection

Peng

et al. 2022

Energies

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High precision short-term photovoltaic (PV) power prediction can reduce the damage associated with large-scale photovoltaic grid-connection to the power system. In this paper, a combination deep learning forecasting method based on variational mode decomposition (VMD), a fast correlation-based filter (FCBF) and bidirectional long short-term memory (BiLSTM) network is developed to minimize PV power forecasting error. In this model, VMD is used to extract the trend feature of PV power, then FCBF is adopted to select the optimal input-set to reduce the forecasting error caused by the redundant feature. Finally, the input-set is put into the BiLSTM network for training and testing. The performance of this model is tested by a case study using the public data-set provided by a PV station in Australia. Comparisons with common short-term PV power forecasting models are also presented. The results show that under the processing of trend feature extraction and feature selection, the proposed methodology provides a more stable and accurate forecasting effect than other forecasting models.

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Using Stacked Long Short Term Memory with Principal Component Analysis for Short Term Prediction of Solar Irradiance based on Weather Patterns

Cited by 25 publications

References 12 publications

A Review on Neural Network Based Models for Short Term Solar Irradiance Forecasting

A Review on Neural Network Based Models for Short Term Solar Irradiance Forecasting

Solar Radiation Forecasting by Pearson Correlation Using LSTM Neural Network and ANFIS Method: Application in the West-Central Jordan

A Short-Term Photovoltaic Power Forecasting Method Combining a Deep Learning Model with Trend Feature Extraction and Feature Selection

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