Accurate one step and multistep forecasting of very short-term PV power using LSTM-TCN model

Renewable Energy, Journal Year: 2023, Volume and Issue: 205, P. 1010 - 1024

Published: Feb. 7, 2023

Language: Английский

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A deep LSTM network for the Spanish electricity consumption forecasting

Neural Computing and Applications, Journal Year: 2022, Volume and Issue: 34(13), P. 10533 - 10545

Published: Feb. 5, 2022

Nowadays, electricity is a basic commodity necessary for the well-being of any modern society. Due to growth in consumption recent years, mainly large cities, forecasting key management an efficient, sustainable and safe smart grid consumer. In this work, deep neural network proposed address short-term, namely, long short-term memory (LSTM) due its ability deal with sequential data such as time-series data. First, optimal values certain hyper-parameters have been obtained by random search metaheuristic, called coronavirus optimization algorithm (CVOA), based on propagation SARS-Cov-2 virus. Then, LSTM has applied predict demand 4-h forecast horizon. Results using Spanish during nine years half measured 10-min frequency are presented discussed. Finally, performance CVOA compared, one hand, that recently published networks (such feed-forward optimized search) temporal fusion transformers sampling algorithm, and, other traditional machine learning techniques, linear regression, decision trees tree-based ensemble techniques (gradient-boosted forest), achieving smallest prediction error below 1.5%.

Language: Английский

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Accurate solar PV power prediction interval method based on frequency-domain decomposition and LSTM model

Energy, Journal Year: 2022, Volume and Issue: 262, P. 125592 - 125592

Published: Sept. 30, 2022

Language: Английский

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Short-term photovoltaic power forecasting using meta-learning and numerical weather prediction independent Long Short-Term Memory models

Renewable Energy, Journal Year: 2023, Volume and Issue: 216, P. 118997 - 118997

Published: July 13, 2023

Short-term photovoltaic (PV) power forecasting is essential for integrating renewable energy sources into the grid as it provides accurate and timely information on expected output of PV systems. Deep learning (DL) networks have shown promising results in this area, but depending weather conditions particularities each system, different DL architectures may perform best. This paper proposes a meta-learning method to improve one-hour-ahead deterministic forecasts systems by dynamically blending base multiple models learn under what model performs Four long short-term memory are used produce production without using numerical predictions, with objective enhance generalizability proposed solution. The accuracy meta-learner evaluated three rooftop Lisbon, Portugal. Results indicate that best at plants, can up 5% over most per plant 4.5% equal-weighted combination forecasts. These improvements statistically significant even larger during peak hours.

Language: Английский

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Metaheuristic-Based Hyperparameter Tuning for Recurrent Deep Learning: Application to the Prediction of Solar Energy Generation

Axioms, Journal Year: 2023, Volume and Issue: 12(3), P. 266 - 266

Published: March 4, 2023

As solar energy generation has become more and important for the economies of numerous countries in last couple decades, it is highly to build accurate models forecasting amount green that will be produced. Numerous recurrent deep learning approaches, mainly based on long short-term memory (LSTM), are proposed dealing with such problems, but most may differ from one test case another respect architecture hyperparameters. In current study, use an LSTM a bidirectional (BiLSTM) data collection that, besides time series values denoting generation, also comprises corresponding information about weather. The research additionally endows hyperparameter tuning by means enhanced version recently metaheuristic, reptile search algorithm (RSA). output tuned neural network compared ones several other state-of-the-art metaheuristic optimization approaches applied same task, using experimental setup, obtained results indicate approach as better alternative. Moreover, best model achieved R2 0.604, normalized MSE value 0.014, which yields improvement around 13% over traditional machine models.

Language: Английский

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A hybrid photovoltaic/wind power prediction model based on Time2Vec, WDCNN and BiLSTM

Energy Conversion and Management, Journal Year: 2023, Volume and Issue: 291, P. 117342 - 117342

Published: July 1, 2023

Language: Английский

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A hybrid deep learning model with an optimal strategy based on improved VMD and transformer for short-term photovoltaic power forecasting

Energy, Journal Year: 2024, Volume and Issue: 295, P. 131071 - 131071

Published: March 22, 2024

Language: Английский

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Short-term PV power prediction based on meteorological similarity days and SSA-BiLSTM

Systems and Soft Computing, Journal Year: 2024, Volume and Issue: 6, P. 200084 - 200084

Published: Feb. 23, 2024

Accurate short-term photovoltaic (PV) power forecasting can reduce the un- certainty of PV generation, which is crucial for grid operation as well energy dispatch. Considering influence seasonal and meteorological factors on prediction, a predic- tion method based similarity day sparrow search algo- rithm bi-directional long memory network combination (SSA-BiLSTM) proposed. Firstly, correlation between generation calculated by using Pearson coefficients, getting strongly correlated affecting generation; afterwards,the historical data are clustered fuzzy C-means clustering to achieve similar clustering; then, best selected from according test features data, Forming training set with original BiLSTM network. SSA algorithm was used find optimal parameters. Finally, Using parameters construct prediction. The experiments were conducted plant in Xinjiang, also compared existing prediction algorithms.The results show that accuracy different weather conditions 33.1 %, 31.9 % 24.1 higher than under same intelligent optimization neural networks, 27.9 24.7 18.0 algorithms Therefore, this paper has better seasons conditions.

Language: Английский

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Multi-step photovoltaic power forecasting using transformer and recurrent neural networks

Renewable and Sustainable Energy Reviews, Journal Year: 2024, Volume and Issue: 200, P. 114479 - 114479

Published: May 17, 2024

Language: Английский

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Forecasting Solar Photovoltaic Power Production: A Comprehensive Review and Innovative Data-Driven Modeling Framework

Energies, Journal Year: 2024, Volume and Issue: 17(16), P. 4145 - 4145

Published: Aug. 20, 2024

The intermittent and stochastic nature of Renewable Energy Sources (RESs) necessitates accurate power production prediction for effective scheduling grid management. This paper presents a comprehensive review conducted with reference to pioneering, comprehensive, data-driven framework proposed solar Photovoltaic (PV) generation prediction. systematic integrating comprises three main phases carried out by seven modules addressing numerous practical difficulties the task: phase I handles aspects related data acquisition (module 1) manipulation 2) in preparation development scheme; II tackles associated model 3) assessment its accuracy 4), including quantification uncertainty 5); III evolves towards enhancing incorporating context change detection 6) incremental learning when new become available 7). adeptly addresses all facets PV prediction, bridging existing gaps offering solution inherent challenges. By seamlessly these elements, our approach stands as robust versatile tool precision real-world applications.

Language: Английский

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