Forecasting of wind speed and power generation prediction using machine learning algorithms

Generally, wind speed prediction plays a vital role in generation of wind power. Lately, wind power generation has developed quickly, and the exactness expectation of wind power generation is vital due to the effect on the security of power frameworks. Notwithstanding, the varieties of wind speeds is incredibly high, making the prediction of wind power generation very troublesome. Initially, correlation analysis of different input parameters is considered and the parameters with the higher correlation are scrutinized and then they are considered to be the ultimate inputs for prediction of wind speed. In this paper, errors are compared between two noted deep learning algorithms namely, Long Short -Term Memory (LSTM) and Gated Recurrent Unit (GRU) and the final conclusion has shown that GRU gives better results compared to LSTM in predicting the wind speed. However, Both LSTM and GRU have their own set of pros and cons.

Improving wind speed forecasting at Adama wind farm II in Ethiopia through deep learning algorithms

Predicting rainfall is a challenging and critical task due to its significant impact on society. Timely and accurate predictions are essential for minimizing human and financial losses. The dependence of approximately 60% of agricultural land in India on monsoon rainfall implies the crucial nature of accurate rainfall prediction. Precise rainfall forecasts can facilitate early preparedness for disasters associated with heavy rains, enabling the public and government to take necessary precautions. In the North-Western Himalayas, where meteorological data are limited, the need for improved accuracy in traditional modeling methods for rainfall forecasting is pressing. To address this, our study proposes the application of advanced machine learning (ML) algorithms, including random forest (RF), support vector regression (SVR), artificial neural network (ANN), and k-nearest neighbour (KNN) along with various deep learning (DL) algorithms such as long short-term memory (LSTM), bi-directional LSTM, deep LSTM, gated recurrent unit (GRU), and simple recurrent neural network (RNN). These advanced techniques hold the potential to significantly improve the accuracy of rainfall prediction, offering hope for more reliable forecasts. Additionally, time series techniques, including autoregressive integrated moving average (ARIMA) and trigonometric, Box-Cox transform, arma errors, trend, and seasonal components (TBATS), are proposed for predicting rainfall across the altitudinal gradients of India’s North-Western Himalayas. This approach can potentially revolutionise how we approach rainfall forecasting, ushering in a new era of accuracy and reliability. The effectiveness and accuracy of the proposed algorithms were assessed using meteorological data obtained from six weather stations at different elevations spanning from 1980 to 2021. The results indicate that DL methods exhibit the highest accuracy in predicting rainfall, as measured by the root mean squared error (RMSE) and mean absolute error (MAE), followed by ML algorithms and time series techniques. Among the DL algorithms, the accuracy order was bi-directional LSTM, LSTM, RNN, deep LSTM, and GRU. For the ML algorithms, the accuracy order was ANN, KNN, SVR, and RF. These findings suggest that altitude significantly affects the accuracy of the models, highlighting the need for additional weather stations in this mountainous region to enhance the precision of rainfall prediction.

Floating offshore wind turbines hold great potential for future solutions to the growing demand for renewable energy production. Thereafter, the prediction of the offshore wind power generation became critical in locating and designing wind farms and turbines. The purpose of this research is to improve the prediction of the offshore wind power generation by the prediction of local wind speed using a Deep Learning technique. In this paper, the future local wind speed is predicted based on the historical weather data collected from National Oceanic and Atmospheric Administration. Then, the prediction of the wind power generation is performed using the traditional methods using the future wind speed data predicted using Deep Learning. The network layers are designed using both Long Short-Term Memory (LSTM) and Bi-directional LSTM (BLSTM), known to be effective on capturing long-term time-dependency. The selected networks are fine-tuned, trained using a part of the weather data, and tested using the other part of the data. To evaluate the performance of the networks, a parameter study has been performed to find the relationships among: length of the training data, prediction accuracy, and length of the future prediction that is reliable given desired prediction accuracy and the training size.

Deep learning-based multistep ahead wind speed and power generation forecasting using direct method

In this study, different kinds of hybrid Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) algorithms with forecasting models including Random Forest (RF), Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM) neural networks, are developed to estimate the mean daily wind speed at the height of 2 m in Ağrı city (WS st12 ), Turkey. In these hybrid models, different layer networks of single and integrated LSTM and GRU models include general single LSTM, general single GRU, simple coupled LSTM-GRU, and novel coupled LSTM with GRU through Addition layer (i.e., LSTM + GRU model) structures are applied. The most effective parameters on the WS st12 , from a list of on-site potential meteorological parameters and wind speed values in its adjacent cities of Ağrı province from Jan 2015–Dec 2019 through the Pearson correlation coefficient method, are determined. In the hybrid CEEMDAN and DNNs-based models, State activation functions (SAF), numbers of hidden neurons (NHN), dropout rates (P-rate), and network structural architect (NSA) as the meta-parameters are tuned for lessening the impact of overfitting/underfitting dilemmas and improving modeling performance. According to the comparison plots, performance evaluation measures, and total learnable parameter (TLP), the novel developed hybrid CEEMDAN-RF-(LSTM + GRU) model is confirmed as the best approach with an R 2 of 0.86 while, in the optimal scenario using the RF model, R 2 was 0.47. Graphical Abstract Based on the graphical snapshot, this study focuses on estimating daily mean wind speed at a 2-meter height in Ağrı, Turkey, using hybrid data-driven models. The research integrates the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) algorithm with advanced forecasting techniques, including Random Forest (RF), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) neural networks. The modeling framework explores various configurations, such as standalone LSTM and GRU, coupled LSTM-GRU structures, and a novel LSTM + GRU model using an Addition layer to enhance predictive accuracy.

Objectives : Photovoltaic power generation which significantly depends on meteorological conditions is intermittent and unstable. Therefore, accurate forecasting of photovoltaic power generation is a challenging task. In this research, random forest (RF), recurrent neural network (RNN), long short term memory (LSTM), and gated recurrent unit (GRU) are proposed and we will find an efficient model for forecasting photovoltaic power generation of photovoltaic power plants.Methods : We used photovoltaic power generation data from photovoltaic power plants at Gamcheonhang-ro, Saha-gu, Busan, and meteorological data from Busan Regional Meteorological Administration. We used solar irradiance, temperature, atmospheric pressure, humidity, wind speed, wind direction, duration of sunshine, and cloud amount as input variables. By applying the trial and error method, we optimized hyperparameters such as estimators in RF, and number of hidden layers, number of nodes, epochs, and validation split in RNN, LSTM, and GRU. We compared proposed models by evaluation indexes such as coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE).Results and Discussion : The best RF at 1,000 of number of decision tree achieved test R2=0.865, test RMSE=16.013, and test MAE=9.656. The best choice of RNN was 6 hidden layers and the number of nodes in each layer was 90. We set the epochs at 450. RNN achieved test R2=0.942, test RMSE=10.530, and test MAE=6.390. To find the best result of LSTM, we used 3 hidden layers, and the number of nodes was 600. The epochs were set to 200. LSTM achieved test R2=0.944, test RMSE=10.29, and test MAE=6.360. GRU was set to 3 hidden layer and the number of nodes was 450. The epochs were set to 500. GRU achieved test R2=0.945, test RMSE=10.189, and test MAE=5.968.Conclusions : We found RNN, LSTM, and GRU performed better than RF, and GRU model showed the best performance. Therefore, GRU is the most efficient model to predict photovoltaic power generation in Busan, Korea.

Under raising pressure of global energy and environmental issues in recent years, wind power has been considered as one of the most promising energy sources owing to with its advantages of being renewable and pollution-free. The accurate and efficient wind speed forecasting (WSF) plays a key role in the generation, distribution, and management of wind power. This study proposes a meta learning based novel hybrid ensemble approach and model for short-term WSF. The ensemble prediction model consists of meta learning part and individual predictor part. The meta learning part is based on a multi-input and multi-output back propagation (BP) neural network (NN) with multiple hidden layers, whereas the individual predictor part is composed of three pre-trained individual predictors based on BP NN, long short-term memory (LSTM) recurrent neural network (RNN), and gated recurrent units (GRU) RNN, respectively. The wind speed value to be predicted can be obtained by weighted summation of two parts of the ensemble prediction model based on historical wind speed data. The innovation in the proposed ensemble WSF model is to build a BP NN and use environmental feature data as input data to generate weight coefficients for updating the individual predictor. In order to illustrate the forecasting performance of the proposed ensemble prediction approach for short-term WSF, the prediction results of the proposed ensemble prediction model are compared with those of several single prediction models and an average coefficient hybrid prediction model under the same conditions. The results illustrate that the meta learning based ensemble prediction model proposed in this study has better forecasting performance in both of prediction accuracy, prediction stability, and data correlation than other WSF models.

One major worldwide issue that has far-reaching effects on environmental stability and the avoidance of natural disasters is climate change. Developing efficient mitigating strategies depends on precise climate change forecasts. To predict important climate change indicators, including temperature fluctuations, greenhouse gas (CO2, N2O, CH4, and SF6) emissions, population dynamics, sea level changes, Arctic Sea ice extent, and Antarctica mass, this study evaluated the predictive capabilities of several Machine Learning (ML) and Deep Learning (DL) techniques. The set of machine learning algorithms includes Multiple Linear Regression (MLR), K-Nearest Neighbors (KNN), Support Vector Machines (SVMs), Artificial Neural Networks (ANN), Random Forest (RF), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Extra Trees (ET). Deep learning models such as Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU) are also included. Their performance was assessed using measures such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared (R2). There were differences in the efficacy of the models found in the study; some models showed more performance, particularly after optimization. The key results indicate that the optimized models with exceptional performance across several indicators are ET, LSTM, GRU, XGBoost, KNN, and RF with R2 values of 0.9617, 0.987, 0.6394, 0.912, 0.9998, 0.999853, 0.994777, 0.949155, and 0.98991, respectively for each dataset. Conversely, SVMs without optimization always perform worse. This Study underlines how important it is to choose and refine models to guarantee precise climate change predictions.

A multivariate ultra-short-term wind speed forecasting model by employing multistage signal decomposition approaches and a deep learning network

E-commerce is a vital component of the world economy, providing people with a simple and convenient method for shopping and enabling businesses to expand into new global markets. Improving e-commerce decision-making by utilizing IoT and machine intelligence represents an important area for the impact of these technologies. Our objective is to elevate online shopping to a new level, making it a practical and genuinely delightful experience for customers. Businesses can acquire valuable insights to improve their operations and sales strategies by employing IoT devices to collect customer behavior and preference data and using machine learning (ML) algorithms to analyze them. In addition, companies can make simple recommendations using machine learning on the collected data. Our creative implementation of ML algorithms extends beyond simple recommendations. It also includes demand forecasting, guaranteeing that popular products are constantly in stock, reducing disappointments, and increasing consumer satisfaction. We applied several ML techniques, including logistic regression, Naïve Bayes, Support Vector Machine (SVM), Random Forest (RF), AdaBoosting, Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM). AdaBoosting outperformed the deep learning (DL) techniques LSTM and GRU and four ML techniques, logistic regression, Naïve Bayes, SVM, and RF, regarding F1 scores, accuracy, precision, and recall. It achieved an accuracy of 88%, an F1-score of 0.927, precision-1 of 0.908, and the ability of identifying true negatives and true positives (recall-0 and recall-1) of 0.569 and 0.947 respectively. Except for SVM, the other ML techniques did not exhibit much performance difference when using the count vectorizer and TD-IDF vectorizer. This study advances e-commerce capabilities through IoT and machine learning and paves the way for a new era of customer-centric, efficient, and adaptive retail strategies.

The exploitation of renewable energy is essential for mitigating climate change and reducing fossil fuel emissions. Wind energy, the most mature technology, is highly dependent on wind speed, and the accurate prediction of the latter substantially supports wind power generation. In this work, various artificial neural networks (ANNs) were developed and evaluated for their wind speed prediction ability using the ERA5 historical reanalysis data for four potential Offshore Wind Farm Organized Development Areas in Greece, selected as suitable for floating wind installations. The training period for all the ANNs was 80% of the time series length and the remaining 20% of the dataset was the testing period. Of all the ANNs examined, the hybrid model combining Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks demonstrated superior forecasting performance compared to the individual models, as evaluated by standard statistical metrics, while it also exhibited a very good performance at high wind speeds, i.e., greater than 15 m/s. The hybrid model achieved the lowest root mean square errors across all the sites—0.52 m/s (Crete), 0.59 m/s (Gyaros), 0.49 m/s (Patras), 0.58 m/s (Pilot 1A), and 0.55 m/s (Pilot 1B)—and an average coefficient of determination (R2) of 97%. Its enhanced accuracy is attributed to the integration of the LSTM and GRU components strengths, enabling it to better capture the temporal patterns in the wind speed data. These findings underscore the potential of hybrid neural networks for improving wind speed forecasting accuracy and reliability, contributing to the more effective integration of wind energy into the power grid and the better planning of offshore wind farm energy generation.

Accurate prediction of short-term wind power generation is of great importance for wind farm operation, the balance of power grid load, and the optimization of bidding strategy on spot market. In general, wind power generation can be predicted using either direct prediction or indirect prediction approaches. The direct approach is to develop a forecasting model based on the historical wind power generation and then predict the future power generation. The indirect approach is to first obtain a wind speed forecasting model, make the prediction of future wind speed, and then convert wind speed forecast to wind power forecast based on the power curve of a wind turbine. This research compares the performances of the two approaches based on the wind speed and power production data of an offshore 2-MW wind turbine. The mature autoregressive integrated moving average (ARIMA)-family forecasting models are adopted for both approaches. In obtaining the forecasting models, no seasonality is found for both wind speed and wind power generation because of the relative short time span of data collection. Therefore, autoregressive and autoregressive moving average models, i.e., the simplified ARIMA models, turn out to be sufficient. The comparison shows that the direct approach produce significantly more accurate forecasts compared with the indirect approach in terms of both mean absolute error and root mean square error. The main reason is that the power curve only considers the averaged deterministic relationship between wind speed and power generation, while in reality the relationship is stochastic in nature. This variability leads to the lower accuracy in predicting wind power generation using the indirect approach.

Improving the accuracy of wind speed forecast can increase wind power generation and better achieve wind energy grid connection. Therefore, a two-stage wind speed prediction model based on Ensemble Empirical Modal Decomposition (EEMD) and the combination prediction of Recurrent Neural Network (RNN), Long Short Term Memory (LSTM), eXtreme Gradient Boosting (XGBOOST), Gate Recurrent Unit (GRU), Temporal Convolutional Network (TCN) is proposed. First, the original wind speed series is separated into Intrinsic Mode Functions (IMFs) using EEMD. Then, RNN, LSTM, XGBOOST, GRU, TCN multiple prediction models are established to learn features from each subsequence and superimpose the prediction results of subsequences. Finally, Particle Swarm Optimization (PSO) is applied to the results of multiple prediction models to assign weights, combined with weight superimposing sequences to achieve higher accuracy and more robust wind speed prediction. Simulation analysis using data from St. Thomas, Virgin Islands wind measurement station to validate the validity of the combined prediction model. The experimental simulation results show that the model proposed in this paper has a good result on increasing wind speed prediction accuracy.

Ensuring regular equipment maintenance is critical for any business that relies on machinery. Predictive maintenance (PdM) is a strategy for scheduling maintenance tasks, with a primary focus on predicting the remaining useful life (RUL) of equipment in advance. This approach helps optimize maintenance schedules, reduce downtime, and detect unexpected faults. Predictions are based on analyzing data collected from the equipment, with machine learning (ML) facilitating these forecasts by training models on historical input data and corresponding outputs. The trained model can then estimate the RUL of the equipment before it reaches the end of its operational capacity. Various ML techniques have been employed for the accurate estimation of the RUL. In this paper, we aim to identify the most effective ML regression methods for PdM and RUL prediction for an auxiliary power unit (APU), focusing on performance indicators such as the root mean squared error (RMSE), the mean absolute error (MAE), and the correlation coefficient ( R ). The process begins with a dataset, followed by feature selection methods such as random forests and normalization during the preprocessing stage. Then, the ML models are trained and evaluated. To assess the effectiveness of the proposed approach, data from the NASA Ames Research Center, along with on‐wing sensor data from the Shenyang Maintenance Base of China Southern Airlines (SYMOB), are used. Six ML algorithms and a hybrid model are employed: Support Vector Machines (SVM), long short‐term memory (LSTM), gated recurrent unit (GRU), decision tree (DT), K‐nearest neighbors (KNNs), gradient boosting trees (GBTs), and a hybrid model (GBT + LSTM). The results for the regression techniques, based on the RMSE and R, are as follows: SVM (37.62, 0.84), LSTM (20.22, 0.91), GRU (31.29, 0.87), DT (17.89, 0.94), KNN (10.98, 0.98), GBT (22.62, 0.97), and (GBT + LSTM) (24.32, 0.96). The KNN method is the most effective approach for this study, as it demonstrates the lowest RMSE and the highest correlation coefficient ( R ) compared to other methods. Therefore, we highly recommend utilizing the KNN technique for PdM analysis of APUs.