Harmonics Forecasting of Renewable Energy System Using Hybrid Model Based on LSTM and ANFIS
Bibliographic record
Abstract
Harmonics forecasting stands as a crucial approach in the development of devices aimed at minimizing harmonics disturbances. The primary objective of this study is to create a hybrid forecasting model that can deliver precise and dependable forecasts for harmonics in Renewable Energy Systems (RES). To achieve this goal, the Adaptive Neuro Fuzzy Inference System (ANFIS) with the Long Short-Term Memory Network (LSTM) are combined in two distinct structured models. In the first model, LSTM is employed in the initial stage and ANFIS in the subsequent one, while the second model follows the reverse order. Additionally, for the generation of harmonics, two renewable generator models are utilized. The first model encompasses a grid-connected Double-Fed Induction Generator (DFIG) driven by a wind turbine and integrated with a Solar Photovoltaic (PV)-based power generator. The second generator model combines a Solar-PV generator with a wind turbine-linked Permanent Magnet Synchronized Generator (PMSG) connected to a shared grid. The harmonics produced by these generator models are used to construct training and testing datasets, which are subsequently employed for generating forecasts using the proposed hybrid forecasting models. The accuracy of forecasting results is verified through a comparison with benchmark studies in the literature. The findings reveal that the model employing ANFIS in the initial stage and LSTM in the second stage (referred to as the ANFIS-LSTM model) consistently yields the best forecasts among all the models tested in this study. Moreover, it exhibits a significant improvement over any of the techniques used in previous literature. Ultimately, this research establishes that both hybrid models proposed outperform the individual forecasting techniques used as benchmarks in terms of accuracy and precision.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".