Near Real-Time Global Solar Radiation Forecasting at Multiple Time-Step Horizons Using the Long Short-Term Memory Network

Huynh, Anh Ngoc-Lan; Deo, Ravinesh C.; An-Vo, Duc-Anh; Ali, Mumtaz; Raj, Nawin; Abdulla, Shahab

doi:10.3390/en13143517

Cited by 34 publications

(17 citation statements)

References 114 publications

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“…According to RRMSE on average and in all prediction horizons, E-GRU outperforms all sub-models. It is important to highlight that according to the RRMSE achieved, E-GRU and all the sub-models can be classified as excellent since they present RRMSE <10% [12], [13].…”

Section: A Resultsmentioning

confidence: 99%

An Ensemble GRU Approach for Wind Speed Forecasting with Data Augmentation

Flores¹,

Tito-Chura²,

Yana-Mamani³

2021

IJACSA

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This paper proposes an ensemble model for wind speed forecasting using the recurrent neural network known as Gated Recurrent Unit (GRU) and data augmentation. For the experimentation, a single wind speed time series is used, from which four augmented time series are generated, which serve to train four GRU sub-models respectively, the results of these submodels are averaged to generate the results of the proposal ensemble model (E-GRU). The results achieved by E-GRU are compared with those of each sub-model, showing that E-GRU outperforms the sub-models. Likewise, the proposal model (E-GRU) is compared with benchmark models without data augmentation such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), showing that E-GRU is much more precise, reaching a difference of around 15% with respect to the Relative Root mean Square Error (RRMSE) and 11% with respect to the Mean Absolute Percentage Error (MAPE).

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Section: A Resultsmentioning

confidence: 99%

An Ensemble GRU Approach for Wind Speed Forecasting with Data Augmentation

Flores¹,

Tito-Chura²,

Yana-Mamani³

2021

IJACSA

View full text Add to dashboard Cite

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“…If the data is multiplied by one, the value remains the same; if the data is multiplied by zero, the value becomes zero and disappears. There are three types of gates [14,[46][47][48][49][50]:…”

Section: Long Short-term Memory (Lstm) Networkmentioning

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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“…Another challenge in collecting global solar radiation data is to deal with typical weather conditions, including rainfall, wind, fog, snow, thunder, humidity, sunshine, etc. Proper installation of solar radiation measuring sensors (Pyranometers) is required for such a data collection, and these sensors can be costly, and many countries do not have sufficient network resources to obtain this data [20]- [22]. In these situations, it is preferred to develop empirical models that can utilize the meteorological data measured by nearby stations [23], [24].…”

Section: Introductionmentioning

confidence: 99%

Advanced Ensemble Model for Solar Radiation Forecasting Using Sine Cosine Algorithm and Newton’s Laws

et al. 2021

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As research in alternate energy sources is growing, solar radiation is catching the eyes of the research community immensely. Since solar energy generation depends on uncontrollable natural variables, without proper forecasting, this energy source cannot be trusted. For this forecasting, the use of machine learning algorithms is one of the best choices. This paper proposed an optimized solar radiation forecasting ensemble model consisting of pre-processing and training ensemble phases. The training ensemble phase works on an advanced sine cosine algorithm (ASCA) using Newton's laws of gravity and motion for objects (agents). ASCA uses sine and cosine functions to update the agent's position/velocity components by considering its mass. The training ensemble model is then developed using the k-nearest neighbors (KNN) regression. The performance of the proposed ensemble model is measured using a dataset from Kaggle (Solar Radiation Prediction, Task from NASA Hackathon). The proposed ASCA algorithm is evaluated in comparison with the Particle Swarm Optimizer (PSO), Whale Optimization Algorithm (WOA), Genetic Algorithm (GA), Grey Wolf Optimizer (GWO), Squirrel Search Algorithm (SSA), Harris Hawks Optimization (HHO), Hybrid Greedy Sine Cosine Algorithm with Differential Evolution (HGSCADE), Hybrid Modified Sine Cosine Algorithm with Cuckoo Search Algorithm (HMSCACSA), Marine Predators Algorithm (MPA), Chimp Optimization Algorithm (ChOA), and Slime Mould Algorithm (SMA). Obtained results of the proposed ensemble model are compared with those of state-of-the-art models, and significant superiority of the proposed ensemble model is confirmed using statistical analysis such as ANOVA and Wilcoxon's rank-sum tests.

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Near Real-Time Global Solar Radiation Forecasting at Multiple Time-Step Horizons Using the Long Short-Term Memory Network

Cited by 34 publications

References 114 publications

An Ensemble GRU Approach for Wind Speed Forecasting with Data Augmentation

An Ensemble GRU Approach for Wind Speed Forecasting with Data Augmentation

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

Advanced Ensemble Model for Solar Radiation Forecasting Using Sine Cosine Algorithm and Newton’s Laws

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