Power Prediction Research Articles

Short-term wind power prediction based on improved variational modal decomposition, least absolute shrinkage and selection operator, and BiGRU networks

Wind energy is a clean resource widely utilized as a renewable energy source. However, due to its inherent strong volatility and the multitude of influencing factors, it is challenging to accurately predict wind power. To address these issues, an IVMD-LASSO-BiGRU model, comprising Improved Variational Mode Decomposition (IVMD), Least Absolute Shrinkage and Selection Operator (LASSO), and Bidirectional Gated Recurrent Unit (BiGRU), is proposed for forecasting. Firstly, based on the sparse prior knowledge of each component constructed in the variational model by VMD, the optimal decomposition mode number K is determined at the inflexion point where the sparsity index shifts from rising to falling. The original wind power sequence is then decomposed into a series of Intrinsic Mode Functions (IMFs) using VMD with the optimal K value, thereby reducing the volatility of the original sequence. Secondly, LASSO is employed to select key features from meteorological data, historical wind power, and IMFs, thereby reducing the data dimension. Subsequently, BiGRU is utilized to fully extract the temporal features of the input data, establishing the mapping between input and output. Experimental results demonstrate that on three different datasets, the R2 values of the proposed forecasting method reach 0.9872, 0.9917, and 0.9941, respectively. Compared to the traditional BiGRU model, the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) are reduced by an average of 57.56% and 58.88%, respectively. Thus, it is evident that the proposed method enhances the accuracy of short-term wind power forecasting, providing a basis for adjusting power generation plans.

Correction: Steam turbine power prediction based on encode-decoder framework guided by the condenser vacuum degree

Correction: Steam turbine power prediction based on encode-decoder framework guided by the condenser vacuum degree

A New Method to Minimize the Standard Deviation and Root Mean Square of the Prediction Error of Single-Optimized IOL Power Formulas.

The purpose of this study was to develop a simplified method to approximate constants minimizing the standard deviation (SD) and the root mean square (RMS) of the prediction error in single-optimized intraocular lens (IOL) power calculation formulas. The study introduces analytical formulas to determine the optimal constant value for minimizing SD and RMS in single-optimized IOL power calculation formulas. These formulas were tested against various datasets containing biometric measurements from cataractous populations and included 10,330 eyes and 4 different IOL models. The study evaluated the effectiveness of the proposed method by comparing the outcomes with those obtained using traditional reference methods. In optimizing IOL constants, minor differences between reference and estimated A-constants were found, with the maximum deviation at -0.086 (SD, SRK/T, and Vivinex) and -0.003 (RMS, PEARL DGS, and Vivinex). The largest discrepancy for third-generation formulas was -0.027mm (SD, Haigis, and Vivinex) and 0.002mm (RMS, Hoffer Q, and PCB00/SN60WF). Maximum RMS differences were -0.021 and +0.021, both involving Hoffer Q. Post-minimization, the largest mean prediction error was 0.726 diopters (D; SD) and 0.043 D (RMS), with the highest SD and RMS after adjustments at 0.529 D and 0.875 D, respectively, indicating effective minimization strategies. The study simplifies the process of minimizing SD and RMS in single-optimized IOL power predictions, offering a valuable tool for clinicians. However, it also underscores the complexity of achieving balanced optimization and suggests the need for further research in this area. The study presents a novel, clinically practical approach for optimizing IOL power calculations.

A novel hybrid model based on multiple influencing factors and temporal convolutional network coupling ReOSELM for wind power prediction

Highly accurate wind power prediction is important for the operation and management of wind farms, energy utilization and power supply. In recent years, in order to further improve prediction accuracy, some researchers have considered utilizing factors such as wind speed and wind direction to establish prediction models. However, this often results in input redundancy due to a lack of technical means. In this paper, a multivariate prediction model for wind power based on variational mode decomposition (VMD), fuzzy entropy (FE), partial auto-correlation function and cross-correlation function (PACF&CCF, abbreviated as PC), improved artificial hummingbird algorithm (IAHA), temporal convolutional network (TCN), and regularized online sequential extreme learning machine (ReOSELM) is proposed. To address the nonlinearity and non-stationarity of the original wind power time series and decrease computational costs, the VMD combined with FE data preprocessing technique is employed. Considering the information redundancy in the multivariate data, the PC technique is utilized to conduct correlation analysis on the input data and select highly correlated features as inputs. When the input is multivariate and high-dimensional, the ReOSELM model struggles to achieve satisfactory prediction performance. Therefore, a new model based on TCN-ReOSELM is constructed. Additionally, to overcome the drawbacks of manual parameter adjustment, an IAHA algorithm is proposed, which integrates Latin hypercube sampling and chaotic local search strategies, to optimize the crucial parameters of TCN-ReOSELM. The experimental results demonstrate that the multivariate method proposed in this paper reduces the RMSE by 31% compared to the univariate method. Specifically, the proposed VMD-FE-PC-TCN-ReOSELM model reduces the RMSE, MAE, and SMAPE by 60–64%, 55–58%, and 10–36%, respectively, compared to the PC-TCN-ReOSELM model.

A New Gated Recurrent Unit Network-Based Wind Turbine Power Prediction

A New Gated Recurrent Unit Network-Based Wind Turbine Power Prediction

Power prediction using high-resolution SCADA data with a farm-wide deep neural network approach

Accurate loss estimation methods with a high level of temporal granularity are necessary to enable the implementation of efficient and adaptable control strategies for wind farms. Predictive models for the power of wind turbines within a wind farm are investigated using high-resolution SCADA data and deep learning methodologies. Traditional physical models offer detailed insights but are computationally expensive. Statistical models face limitations in handling wind energy variability. In this study, deep learning models are explored to capture spatial and temporal dynamics, with four models developed: Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), Long Short-Term Memory network (LSTM), and a hybrid CNN-LSTM model. SCADA data from an offshore wind farm is preprocessed, anomalies removed, and annotated based on operational regions. The models are trained, validated, and tested, demonstrating improved accuracy over baseline methods. The hybrid model, incorporating spatial and temporal information, yields the highest predictive performance, showcasing the significance of these dimensions in wind power prediction.

Modeling the effect of wind speed and direction shear on utility‐scale wind turbine power production

AbstractWind speed and direction variations across the rotor affect power production. As utility‐scale turbines extend higher into the atmospheric boundary layer (ABL) with larger rotor diameters and hub heights, they increasingly encounter more complex wind speed and direction variations. We assess three models for power production that account for wind speed and direction shear. Two are based on actuator disc representations, and the third is a blade element representation. We also evaluate the predictions from a standard power curve model that has no knowledge of wind shear. The predictions from each model, driven by wind profile measurements from a profiling LiDAR, are compared to concurrent power measurements from an adjacent utility‐scale wind turbine. In the field measurements of the utility‐scale turbine, discrete combinations of speed and direction shear induce changes in power production of −19% to +34% relative to the turbine power curve for a given hub height wind speed. Positive speed shear generally corresponds to over‐performance and increasing magnitudes of direction shear to greater under‐performance, relative to the power curve. Overall, the blade element model produces both higher correlation and lower error relative to the other models, but its quantitative accuracy depends on induction and controller sub‐models. To further assess the influence of complex, non‐monotonic wind profiles, we also drive the models with best‐fit power law wind speed profiles and linear wind direction profiles. These idealized inputs produce qualitative and quantitative differences in power predictions from each model, demonstrating that time‐varying, non‐monotonic wind shear affects wind power production.

Experimental and numerical comparison of aerodynamics for a wind turbine rotor model under turbulent inflow conditions

Computational prediction of aerodynamics for a two-bladed wind turbine rotor model at the profile, blade, and rotor scale are compared to wind tunnel experimental data. Wind tunnel tests are carried out in the large subsonic wind tunnel at the University of Orleans with an instrumented rotor model to obtain power and thrust coefficients, bending moments and pressure chordwise distributions. A passive uniform turbulence grid is used to obtain a turbulence intensity of 3.8% at the front of the rotor. A k − ω SST unsteady Reynolds-averaged Navier-Stockes (URANS) simulation with the ISIS-CFD flow solver is conducted with a fully resolved rotor, with and without automatic grid refinement. The focus of this study is to examine the prediction capabilities of the simulation for various physical variables. The simulation demonstrates a relatively good prediction of power and thrust coefficients compared with experimental data with a maximum scatter of 8.5%. The prediction of the pressure coefficient distribution at three different radial positions is satisfactory, but shows discrepancies when it comes to accurately predicting flow separation and Reynolds effects.

Wind power prediction based on NGO-LSTM

Abstract At present, wind energy is mainly utilized through wind power grid connections. However, because of the uncertainty of wind energy, the grid connection will inevitably have an impact on the system. In order to adjust the grid dispatching plan in real time and improve the grid’s capacity to absorb wind power, a short-term wind speed prediction method based on NGO-LSTM was proposed. Taking the measured data of wind farms as an example, the prediction accuracy of the proposed method is compared with that of existing algorithms. Experimental results show that the R2 of the NGO-LSTM coupling model is closest to 1, and the RMSE, MAE, and MAPE are all smaller than the other two models, verifying the effectiveness and advanced nature of the proposed method.

Particle swarm optimization-extreme learning machine model combined with the ADA boost algorithm for short-term wind power prediction

In our proposed approach, we integrate ADA boosting with particle swarm optimization-extreme learning machine (PSO-ELM) to enhance the accuracy of wind power estimation, addressing the inherent unpredictability and variability in wind energy. Initially, we refine the thresholds and input weights of the extreme learning machine (ELM) and then construct the PSO-ELM prediction model. ADA Boost is utilized to generate multiple weak predictors, each comprising a distinct hidden layer node. The PSO technique is then employed to optimize the input weights and thresholds for each weak predictor. The final forecast is attained by amalgamating and weighting the outcomes from each weak predictor using a robust wind power forecast model. Experimental validation utilizing data from Turkish wind turbines underscores the efficacy of our approach. Comparative analysis against contemporary techniques such as ensemble learning models and optimal neural networks reveals that our ADA-PSO-ELM model demonstrates superior accuracy and generalizability in predicting wind power output under real-world conditions. The proposed approach offers a promising framework for addressing the challenges associated with wind power estimation, thereby facilitating more reliable and efficient utilization of wind energy resources.

Prediction and scheduling of multi-energy microgrid based on BiGRU self-attention mechanism and LQPSO

To predict renewable energy sources such as solar power in microgrids more accurately, a hybrid power prediction method is presented in this paper. First, the self-attention mechanism is introduced based on a bidirectional gated recurrent neural network (BiGRU) to explore the time-series characteristics of solar power output and consider the influence of different time nodes on the prediction results. Subsequently, an improved quantum particle swarm optimization (QPSO) algorithm is proposed to optimize the hyperparameters of the combined prediction model. The final proposed LQPSO-BiGRU-self-attention hybrid model can predict solar power more effectively. In addition, considering the coordinated utilization of various energy sources such as electricity, hydrogen, and renewable energy, a multi-objective optimization model that considers both economic and environmental costs was constructed. A two-stage adaptive multi- objective quantum particle swarm optimization algorithm aided by a Lévy flight, named MO-LQPSO, was proposed for the comprehensive optimal scheduling of a multi-energy microgrid system. This algorithm effectively balances the global and local search capabilities and enhances the solution of complex nonlinear problems. The effectiveness and superiority of the proposed scheme are verified through comparative simulations.

A new power prediction model for wind power generation

Abstract A wind power generation forecast model based on WOA-SVM is presented to exploit effective information in different data sets completely. This model addresses the difficulties associated with parameter selection, low prediction accuracy, and susceptibility to local optima in short-term wind energy prediction. The model systematically investigates the relationship between various wind parameters (mean wind speed, maximum wind speed, minimum wind speed, mean wind direction, and mean hull position) and wind power. It then evaluates the model’s performance using mean absolute error and coefficient of determination. The predictive outcomes of the WOA-SVM model are contrasted with those of the SVM model, PSO-SVM model, and extreme learning machine (ELM) model, utilising authentic data from a wind farm located in northern China in the year 2021. The overall evaluation indicates that the WOA-SVM model outperforms the other models in short-term wind energy prediction, demonstrating superior prediction accuracy.

Research on wind farm power forecasting based on IGWO-CNN

Abstract An enhanced convolutional neural network (CNN) has been proposed to address the issue of low accuracy in wind farm power prediction using traditional methods, which leverages an improved version of the Gray Wolf Optimizer (IGWO). The standard Gray Wolf Optimizer (GWO) exhibits a linear change in the search range, which can limit its dynamic convergence capabilities. By incorporating nonlinear convergence factors and dynamic convergence strategies, the improved algorithm modulates the global search pace, effectively preventing it from getting stuck in local optima. The refined IGWO is subsequently applied to fine-tune the optimal CNN architecture, mitigating the uncertainty typically associated with the structural configuration of CNNs. When predicting the actual power measurements from a wind plant in Inner Mongolia, the results indicate that the IGWO-based CNN method outperforms both the conventional GWO-CNN approach and the standalone CNN regarding prediction accuracy.

DESIGN AND DEVELOPMENT OF TIDAL ENERGY GENERATION SYSTEM

Sustainable energy generation is becoming increasingly important due to the expected limitations in current energy resources and to reduce pollution. Wave energy generation has seen significant development in recent years. Renewable power generation has become increasingly vital in addressing global energy challenges and mitigating the impacts of climate change. Various forms of renewable energy sources, such as solar, wind, and hydroelectric power, have gained prominence in the pursuit of sustainable energy solutions. Among these, tidal power stands out as a promising option due to its predictability and minimal environmental impact. This abstract explores the significance of renewable power generation and introduces the concept of tidal power generation, focusing on the innovative technology of tidal floater turbines. It delves into their construction and operational principles, illustrating their potential as a sustainable energy source. Ultimately, this abstract underscores the growing importance of harnessing tidal power as a clean and reliable source of electricity for the future. Tidal floater turbines represent an innovative system for harnessing the energy potential of ocean tides. These turbines are strategically constructed on the surface of the sea, where the dynamic ebb and flow of tides generate kinetic energy. The turbines consist of underwater rotors connected to electrical generators, which are capable of converting the mechanical energy of tidal currents into electricity. The operation of tidal floater turbines is driven by the predictable and consistent nature of tidal cycles, making them a dependable source of renewable energy. By exploiting the immense power of the ocean's tides, these turbines offer a sustainable solution for meeting the world's growing energy demands. The result from wave simulator using a real wave profile captured at a location in UK using an ultra sound system it was seen that ±0.8m wave at 10sec time period, produce a condition power output of approximate 22KW at optimum load condition for the tested 3 phase 44KW permanent magnet generator type STK500 the result indicate that this new technology could provide an effective and low cost method of generating electricity from waves. In conclusion, the utilization of tidal power generation, specifically through the innovative technology of tidal floater turbines, holds significant promise in the realm of renewable energy. As society seeks to reduce its carbon footprint and transition towards cleaner energy sources, the predictability and consistency of tidal power make it an attractive option. Moreover, the construction of these turbines on the sea's surface minimizes ecological disruptions, further emphasizing their environmental sustainability. With ongoing research and development, tidal power generation has the potential to play a pivotal role in our quest for a greener and more sustainable energy future.

Two-stage correction prediction of wind power based on numerical weather prediction wind speed superposition correction and improved clustering

The unavoidable wind power prediction error poses a challenge for wind power to participate in day-ahead scheduling plan. This paper proposes a two-stage correction method considering NWP wind speed and power correction. Firstly, this paper fully considers the historical similarity of NWP wind speed, and adopts the improved clustering distance for segment matching and wind speed correction based on the extracted most relevant trend component, which enhances the utilization of NWP information and avoids the randomness and uncertainty of the intelligent correction method, and gram angular field (GAF) and stacked autoencoder (SAE) are used for feature extraction and conditional variational autoencoder (CVA) is used to generate specific samples to ensure the adequacy of the clustering process. Secondly, considering the historical similarity of error of predicted power, the power is further corrected based on the proposed weighted double-constraint to realize the secondary calibration. The Proposed method is applied to several wind farms in China to verify its effectiveness. The results show that the proposed method reduces the NRMSE by 3.82 % and NMAE by 3.40 % compared with direct prediction in the wind farms in western Inner Mongolia, which is important for promoting wind power consumption and maintaining the safe and stable operation of the power system.

Wind Power Prediction Based on EMD-KPCA-BiLSTM-ATT Model

In order to improve wind power utilization efficiency and reduce wind power prediction errors, a combined prediction model of EMD-KPCA-BilSTM-ATT is proposed, which includes a data processing method combining empirical mode decomposition (EMD) and kernel principal component analysis (KPCA), and a prediction model combining bidirectional long short-term memory (BiLSTM) and an attention mechanism (ATT). Firstly, the influencing factors of wind power are analyzed. The quartile method is used to identify and eliminate the original abnormal data of wind power, and the linear interpolation method is used to replace the abnormal data. Secondly, EMD is used to decompose the preprocessed wind power data into Intrinsic Mode Function (IMF) components and residual components, revealing the changes in data signals at different time scales. Subsequently, KPCA is employed to screen the key components as the input of the BiLSTM-ATT prediction model. Finally, a prediction is made taking an actual wind farm in Anhui Province as an example, and the results show that the EMD-KPCAM-BiLSTM-ATT combined model has higher prediction accuracy compared to the comparative model.

Dual NWP wind speed correction based on trend fusion and fluctuation clustering and its application in short-term wind power prediction

With the continuous growth of grid-connected installed capacity of wind power, the inevitable gap, volatility and randomness of wind power pose challenges to the stability of real-time operation of power system. Accurate wind power prediction (WPP) can effectively ensure the safe and stable operation of power system. At present, the main input of short-term power prediction based on data-driven model is numerical weather prediction (NWP), and its prediction accuracy leads to the failure to effectively improve the accuracy of short-term power prediction. To solve this problem, this paper proposes a dual NWP wind speed (WS) correction method based on trend fusion and fluctuation clustering. First, the WS trend is used to construct a new input feature, and the NWP WS error distribution is determined to establish a more correct and stable mapping relationship with the NWP WS error. Secondly, the error is decomposed by Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), and the trend and fluctuation components are defined by Pearson coefficient. The trend curve features in the short 24h period were extracted from the two dimensions of time and value, and the trend and fluctuation curve databases under different scenarios were formed by K-Medoids clustering. Finally, the trend component is modified by the Attention-GRU model, and the corresponding trend component is searched and matched from the database to correct the fluctuation component. The NWP WS is modified by the superposition of the two components and the short-term WPP is made. Two wind farms in Mengxi, China were used for example analysis, and the prediction accuracy was improved by 10 % and 13.1 % respectively, which demonstrated the effectiveness of the proposed method.

Computational prediction of new therapeutic effects of probiotics

Probiotics are living microorganisms that provide health benefits to their hosts, potentially aiding in the treatment or prevention of various diseases, including diarrhea, irritable bowel syndrome, ulcerative colitis, and Crohn’s disease. Motivated by successful applications of link prediction in medical and biological networks, we applied link prediction to the probiotic-disease network to identify unreported relations. Using data from the Probio database and International Classification of Diseases-10th Revision (ICD-10) resources, we constructed a bipartite graph focused on the relationship between probiotics and diseases. We applied customized link prediction algorithms for this bipartite network, including common neighbors, Jaccard coefficient, and Adamic/Adar ranking formulas. We evaluated the results using Area under the Curve (AUC) and precision metrics. Our analysis revealed that common neighbors outperformed the other methods, with an AUC of 0.96 and precision of 0.6, indicating that basic formulas can predict at least six out of ten probable relations correctly. To support our findings, we conducted an exact search of the top 20 predictions and found six confirming papers on Google Scholar and Science Direct. Evidence suggests that Lactobacillus jensenii may provide prophylactic and therapeutic benefits for gastrointestinal diseases and that Lactobacillus acidophilus may have potential activity against urologic and female genital illnesses. Further investigation of other predictions through additional preclinical and clinical studies is recommended. Future research may focus on deploying more powerful link prediction algorithms to achieve better and more accurate results.

Investigation on the adsorption characteristics of activated carbons in the sub-Kelvin 4He sorption cooler

The adsorption characteristics of the selected activated carbon have an important effect on the performance of the sub-Kelvin helium sorption cooler. In this paper, a test system for the adsorption amount of the carbons based on a G-M cooler was built, which has the advantages of being detachable and high gas seal performance. The adsorption isotherm of the three carbons (5.3 K-40 K, 0–0.6 MPa) for 4He were experimentally tested, and the data were fitted according to the Dubinin theory. In addition, the calculation methods of sorption cooler were summarized and a model of cooling power prediction was established. Based on the above theoretical model and experimental data, a carbon with high specific surface area and excellent adsorption properties was selected as filler, and a prototype of 4He sorption cooler was developed. The lowest temperature and holding time of the prototype are 923 mK and 8.47 h, respectively, in good agreement with the theoretical model. The model and the experimental method can provide reference for the design of sorption coolers at sub-Kelvin temperatures.

Prediction of main engine power of oil tankers using artificial intelligence algorithms

In the preliminary ship design, the accurate determination of a vessel’s main engine power is one of the most critical aspects next to service speed, main particulars, and cargo capacity. However, this task can be quite intricate due to its reliance on an extremely great number of influencing factors. In the research that is presented in this paper dataset of 357 oil tankers was gathered and developed to research the idea in which genetic programming is applied to the mentioned dataset to obtain mathematical equations (MEs) that can estimate the ship’s main engine power with high accuracy. The highest estimation accuracy of MEs is achieved by tuning the GP hyperparameter values through the random hyperparameter search (RHS) method. The initial dataset was divided into train and test datasets in a 70:30 ratio. The train dataset was used to train GP in a 5-fold cross-validation process and after the process was done the obtained MEs were evaluated on the test dataset. To evaluate the GP training testing process several evaluation metrics were used i.e., coefficients of determination (R2), mean absolute error (MAE), root mean square error (RMSE), and length of obtained MEs. The conducted investigation showed that GP generated MEs that can estimate ship main engine power with high accuracy.