Short-Term Wind Energy Forecasting Using Deep Learning-Based Predictive Analytics

Shabbir, Noman; K黷t, Lauri; Jawad, Muhammad; Husev, Oleksandr; Rehman, Ateeq Ur; Gardezi, Akber Abid; Shafiq, Muhammad; Choi, Jin-Ghoo

doi:10.32604/cmc.2022.024576

Cited by 12 publications

(6 citation statements)

References 34 publications

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“…The LSTM algorithm has become one of the essential tools in NILM due to its ability to overcome the limitations of traditional Recurrent Neural Networks (RNNs), especially in handling long-term dependencies and gradient vanishing issues [49]. LSTMs are particularly favored for NILM tasks because they excel at capturing the inherent long-term dependencies present in time series data.…”

Section: Long Short-term Memory Network (Lstm)mentioning

confidence: 99%

Comparative Analysis of Machine Learning Techniques for Non-Intrusive Load Monitoring

Shabbir,

Vassiljeva,

Nourollahi Hokmabad

et al. 2024

Electronics

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Non-intrusive load monitoring (NILM) has emerged as a pivotal technology in energy management applications by enabling precise monitoring of individual appliance energy consumption without the requirements of intrusive sensors or smart meters. In this technique, the load disaggregation for the individual device is accrued by the recognition of their current signals by employing machine learning (ML) methods. This research paper conducts a comprehensive comparative analysis of various ML techniques applied to NILM, aiming to identify the most effective methodologies for accurate load disaggregation. The study employs a diverse dataset comprising high-resolution electricity consumption data collected from an Estonian household. The ML algorithms, including deep neural networks based on long short-term memory networks (LSTM), extreme gradient boost (XgBoost), logistic regression (LR), and dynamic time warping with K-nearest neighbor (DTW-KNN) are implemented and evaluated for their performance in load disaggregation. Key evaluation metrics such as accuracy, precision, recall, and F1 score are utilized to assess the effectiveness of each technique in capturing the nuanced energy consumption patterns of diverse appliances. Results indicate that the XgBoost-based model demonstrates superior performance in accurately identifying and disaggregating individual loads from aggregated energy consumption data. Insights derived from this research contribute to the optimization of NILM techniques for real-world applications, facilitating enhanced energy efficiency and informed decision-making in smart grid environments.

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Section: Long Short-term Memory Network (Lstm)mentioning

confidence: 99%

Comparative Analysis of Machine Learning Techniques for Non-Intrusive Load Monitoring

Shabbir,

Vassiljeva,

Nourollahi Hokmabad

et al. 2024

Electronics

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“…In 2022, Noman et al [20] have implemented a new method using energy prediction based on DL in the renewable energy resource. Due to the number of factors, the stability of the wind energy was featured based on the "season, climate area, weather and time of the day".…”

Section: A Related Workmentioning

confidence: 99%

“…But, slow computation leads to training difficulties and high adversarial attacks, which leads to misclassification in DL models. RNN-LSTM [20] can approximate arbitrary nonlinear systems with high precision. But, the model's Reliability, flexibility, and power quality are low, and the vanishing gradient problem is high in this model.…”

Section: B Research Gaps and Challengesmentioning

confidence: 99%

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A Novel Renewable Power Generation Prediction Through Enhanced Artificial Orcas Assisted Ensemble Dilated Deep Learning Network

Che,

Amirthasaravanan,

Al-Razgan

et al. 2024

IEEE Access

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The different energy resource generation tends to have high-level variation, making the power supply complex for the end-users. Because of the intermittent nature, the variations occur by time, weather conditions, and output energy. Hence, this research aims to develop a new "Renewable Power Generation Prediction (RPGP)" model using Deep Learning (DL) to give the end user a reliable power supply. The data aggregation process initially accumulated the data in a normalized and structured format. Then, the data cleaning and scaling are performed to decrease the outliers and varying ranges of values. A higher-order statistical feature was attained from the cleaned and scaled data. This statistical feature was given to "Optimal Weight Computation Ensemble Dilated Deep Network (OWC-EDDNet)" to predict generated power. In this EDDLNet, networks such as "Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Deep Belief Networks (DBN), and Deep Neural Networks (DNN)" are employed to predict the renewable generated power. Finally, the prediction score attained from all deep networks is multiplied by the optimized weight to get the final prediction outcome, where the weights are optimally determined with the support of the Enhanced Artificial Orcas Algorithm (EAOA). The extensive empirical results were analyzed among traditional algorithms and prediction models to showcase the efficacy of the designed energy generation prediction scheme.

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“…The authors in [19] performed a detailed study of wind resource forecasting through the Weibull distribution (WD) directly or through integration with other mechanisms, as presented in the recent literature. In [20,21], the RNN-LSTM algorithm was proposed for day-ahead wind energy generation.…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting of Wind Speed and Power through FFNN and CFNN Using HPSOBA and MHPSO-BAACs Techniques

Usman¹,

Arif²,

Usman³

et al. 2022

Electronics

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Renewable Energy Sources are an effective alternative to the atmosphere-contaminating, rapidly exhausting, and overpriced traditional fuels. However, RESs have many limitations like their intermittent nature and availability at far-off sites from the major load centers. This paper presents the forecasting of wind speed and power using the implementation of the Feedforward and cascaded forward neural networks (FFNNs and CFNNs, respectively). The one and half year’s dataset for Jhimpir, Pakistan, is used to train FFNNs and CFNNs with recently developed novel metaheuristic optimization algorithms, i.e., hybrid particle swarm optimization (PSO) and a Bat algorithm (BA) named HPSOBA, along with a modified hybrid PSO and BA with parameter-inspired acceleration coefficients (MHPSO-BAAC), without and with the constriction factor (MHPSO-BAAC-χ). The forecasting results are made for June–October 2019. The accuracy of the forecasted values is tested through the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). The graphical and numerical comparative analysis was performed for both feedforward and cascaded forward neural networks that are tuned using the mentioned optimization techniques. The feedforward neural network was achieved through the implementation of HPSOBA with a mean absolute error, mean absolute percentage error, and root mean square error of 0.0673, 6.73%, and 0.0378, respectively. Whereas for the case of forecasting through a cascaded forward neural network, the best performance was attained by the implementation of MHPSO-BAAC with a MAE, MAPE and RMSE of 0.0112, 1.12%, and 0.0577, respectively. Thus, the mentioned neural networks provide a more accurate prediction when trained and tuned through the given optimization algorithms, which is evident from the presented results.

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Short-Term Wind Energy Forecasting Using Deep Learning-Based Predictive Analytics

Cited by 12 publications

References 34 publications

Comparative Analysis of Machine Learning Techniques for Non-Intrusive Load Monitoring

Comparative Analysis of Machine Learning Techniques for Non-Intrusive Load Monitoring

A Novel Renewable Power Generation Prediction Through Enhanced Artificial Orcas Assisted Ensemble Dilated Deep Learning Network

Forecasting of Wind Speed and Power through FFNN and CFNN Using HPSOBA and MHPSO-BAACs Techniques

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