Prediction Of Power Production Research Articles

Effects of wind shear and thrust coefficient on the induction zone of a porous disk: A wind tunnel study

AbstractNeglecting the velocity reduction in the induction zone of wind turbines can lead to overestimates of power production predictions, and, thus, of the annual energy production for a wind farm. An experimental study on the induction zone associated with wind turbine operations is performed in the boundary‐layer test section of the BLAST wind tunnel at UT Dallas using stereo particle image velocimetry. This experiment provides a detailed quantification of the wind speed decrease associated with the induction zone for two different incoming flows, namely, uniform flow and boundary layer flow. Operations of wind turbines in different regions of the power curve are modeled in the wind tunnel environment with two porous disks with a solidity of 50.4% and 32.3%, which correspond to thrust coefficients of 0.71 and 0.63, respectively. The porous disks are designed to approximate the wake velocity profiles previously measured for utility‐scale wind turbines through scanning wind LiDARs. The results show that the streamwise velocity at one rotor diameter upwind of both disks decreases 1% more for the boundary layer flow than for the uniform flow. Further, the effect of shear in front of the disk with a higher thrust coefficient can be observed until 1.75 rotor diameter upwind of the disk, whereas for the disk with a lower thrust coefficient, the effect of shear becomes negligible at 1.25 rotor diameter upwind. It is found that at one rotor diameter upwind, for both incoming flows, the disk having a higher thrust coefficient has 2% more velocity reduction than the lower‐thrust‐coefficient disk. The results suggest that the variability in wind shear and rotor thrust coefficient, which is encountered during typical operations of wind turbines, should be considered for the development of improved models for predictions of the rotor induction zone, the respective cumulative effects in the presence of multiple turbines, namely, wind farm blockage, and more accurate predictions of wind farm power capture.

A Hybrid Approach for the Ensembles of Neural Networks for Solar Power Forecasting

In this study, a unique hybrid method to solar power forecasting is presented. This strategy makes use of ensemble approaches, which include Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory networks (LSTMs). A more accurate prediction of solar power production may be achieved using the hybrid ensemble, which takes advantage of the specific characteristics of each neural network design. Extensive trials conducted using datasets taken from the actual world demonstrate that the suggested technique is superior to both individual neural network models and conventional methods of predicting. Therefore, the ensemble is a feasible option for practical solar power forecasting applications, helping to the effective utilisation of renewable energy resources. Its flexibility to changing weather conditions, interpretability, and superior accuracy position it as a promising solution.

Power Production, Inter- and Intra-Array Wake Losses from the U.S. East Coast Offshore Wind Energy Lease Areas

There is an urgent need to develop accurate predictions of power production, wake losses and array–array interactions from multi-GW offshore wind farms in order to enable developments that maximize power benefits, minimize levelized cost of energy and reduce investment uncertainty. New, climatologically representative simulations with the Weather Research and Forecasting (WRF) model are presented and analyzed to address these research needs with a specific focus on offshore wind energy lease areas along the U.S. east coast. These, uniquely detailed, simulations are designed to quantify important sources of wake-loss projection uncertainty. They sample across different wind turbine deployment scenarios and thus span the range of plausible installed capacity densities (ICDs) and also include two wind farm parameterizations (WFPs; Fitch and explicit wake parameterization (EWP)) and consider the precise WRF model release used. System-wide mean capacity factors for ICDs of 3.5 to 6.0 MWkm−2 range from 39 to 45% based on output from Fitch and 50 to 55% from EWP. Wake losses are 27–37% (Fitch) and 11–19% (EWP). The discrepancy in CF and wake losses from the two WFPs derives from two linked effects. First, EWP generates a weaker ‘deep array effect’ within the largest wind farm cluster (area of 3675 km2), though both parameterizations indicate substantial within-array wake losses. If 15 MW wind turbines are deployed at an ICD of 6 MWkm−2 the most heavily waked wind turbines generate an average of only 32–35% of the power of those that experience the freestream (undisturbed) flow. Nevertheless, there is no evidence for saturation of the resource. The wind power density (electrical power generation per unit of surface area) increases with ICD and lies between 2 and 3 Wm−2. Second, EWP also systematically generates smaller whole wind farm wakes. Sampling across all offshore wind energy lease areas and the range of ICD considered, the whole wind farm wake extent for a velocity deficit of 5% is 1.18 to 1.38 times larger in simulations with Fitch. Over three-quarters of the variability in normalized wake extents is attributable to variations in freestream wind speeds, turbulent kinetic energy and boundary layer depth. These dependencies on meteorological parameters allow for the development of computationally efficient emulators of wake extents from Fitch and EWP.

Long-term power prediction of photovoltaic panels based on meteorological parameters and support vector machine

<span>Solar energy is the most generally accessible energy in the entire globe. Proper solar panel maintenance is necessary to reduce reliance on imported energy. Continuous monitoring of the solar panel's power output is required. The deployment of internet of things (IoT) monitoring of solar panels for maintenance is the basis for the current research. A multi-variable long-term photovoltaic (PV) power production prediction approach based on support vector machine (SVM) is developed in this study with the aim of completely evaluating the influence of PV panels performance and actual operational state factors on the power generation efficiency. This study examines the use of SVM and climatic factors to forecast the long-term output of power from solar panels. A solar power facility in a semi-arid area provided the data utilized in this investigation. Temperature, humidity, wind speed, and sun radiation are some of the meteorological variables that were considered in the study. To anticipate the power generation of the panels, the SVM is trained using the climatic factors and the power generation data. The findings demonstrate that the SVM model consistently predicts the panels' long-term power generation with a high degree of accuracy.</span>

Predicting Steam Turbine Power Generation: A Comparison of Long Short-Term Memory and Willans Line Model

This study focuses on using machine learning techniques to accurately predict the generated power in a two-stage back-pressure steam turbine used in the paper production industry. In order to accurately predict power production by a steam turbine, it is crucial to consider the time dependence of the input data. For this purpose, the long-short-term memory (LSTM) approach is employed. Correlation analysis is performed to select parameters with a correlation coefficient greater than 0.8. Initially, nine inputs are considered, and the study showcases the superior performance of the LSTM method, with an accuracy rate of 0.47. Further refinement is conducted by reducing the inputs to four based on correlation analysis, resulting in an improved accuracy rate of 0.39. The comparison between the LSTM method and the Willans line model evaluates the efficacy of the former in predicting production power. The root mean square error (RMSE) evaluation parameter is used to assess the accuracy of the prediction algorithm used for the generator’s production power. By highlighting the importance of selecting appropriate machine learning techniques, high-quality input data, and utilising correlation analysis for input refinement, this work demonstrates a valuable approach to accurately estimating and predicting power production in the energy industry.

Power generation prediction of a geothermal-thermoelectric hybrid system using intelligent models

This study focuses on the most effective use of energy resources, which is the main goal of sustainable energy systems. The first contribution of the research is to ensure that the waste and geothermal fluids generated during the geothermal energy production process are evaluated in the most efficient way and reused for energy production. For this purpose, a geothermal-thermoelectric hybrid experimental system has been designed to produce electrical energy through heat exchange and how waste thermal energy could be reused in this process has been examined. Experimental tests have been performed under electrical load and in the presence of real-time uncertainties such as temperature changes, thermal conductivity, electrical noise, heat losses and random variations in thermal interaction. The second main contribution of the study is to predict and evaluate different power generation performances in the Thermoelectric Generator (TEG) system, which is affected by the mentioned experimental limitations based on artificial intelligence models. In addition, the optimal prediction performance of artificial intelligence models using hyper-parameter optimization has also been compared. Experimental results have shown that intelligent models are a successful tool for energy prediction in geothermal power generation. As a result of the hyper-parameter optimization, each model is improved by ∼5 % to ∼30 % in various error metrics. However, it was observed that the decision tree-based learning techniques resulted in ∼46 % to ∼96 % less inaccurate predictions on various error metrics compared to other methods. Power production predictions obtained using intelligent models have been obtained with 98.7 % accuracy with LightGBM learning algorithms. Most of the models used to classify different hot and cold water levels exhibit very high classification performance. The results show the successful integration of artificial intelligence models in effectively utilizing waste thermal energy and adopting a sustainable approach to energy production.

Neural Networks Modelling for Thermal Plants Production Planning

With the power systems progress, its complexity grows and there is a greater need for good production organization and the need for information that would help in power system planning. As thermal power plants are responsible for covering the base load, the prediction of its production can be used significantly for good power system organization. This paper represents the use of artificial neural networks for the prediction of thermal plants power production. The data used for neural networks training are measurements of thermal power plant production in power systems of Serbia. Trained neural network can be used for prediction of production on yearly basis or for shorter, hourly or daily prediction.

Upsampling wave temporal resolution: Investigating wave parameters and the influence on WEC power performance

Power production of wave energy converters (WEC) predicted in the time domain utilize wave resource parameters and time-domain hydrodynamic model simulations of the WEC. While the hydrodynamic model provides high temporal resolution of power production (10’s of Hz), the wave resource parameters are often based on frequency-domain calculations with temporal resolution of 30 minutes to an hour. However, real ocean wave conditions vary on much shorter time scales. Relying on frequency-domain calculations will not be sufficient to capture the short-term variability and accurately predict WEC power production for a standardized methodology that follows power system requirements. These requirements need forecasted data with high sampling frequency for accurate energy predictions. Looking specifically at resource characterization, high temporal resolution datasets are not publicly available or do not exist for many coastal locations. Due to data availability, low temporal resolution datasets are being used in a majority of studies to generate representative wave conditions as inputs to numerical simulations. Representative wave conditions are used to generate wave spectrums. The issue with this practice is spectrums are then used to predict the efficiency of systems that will not accurately capture the variability of waves in short timeframes. Creating a standardized methodology to increase the temporal resolution of metaocean conditions to inform model development can provide better forecasting of power production. Random amplitude, Fourier coefficient methods have been suggested for WEC simulations of finite durations to improve the observed variability in wave heights and power production. Variability using this method does increase for finite durations compared to the commonly used deterministic amplitude method. In this paper we will investigate the influence of wave parameters (significant wave height, maximum wave height, and energy period) on the prediction of WEC power production. A better understanding of the influence of these parameters will provide a path towards future standardization methodology for resource inputs for time-domain modeling.

Validation of turbulence intensity as simulated by the Weather Research and Forecasting model off the US northeast coast

Abstract. Turbulence intensity (TI) is often used to quantify the strength of turbulence in wind energy applications and serves as the basis of standards in wind turbine design. Thus, accurately characterizing the spatiotemporal variability in TI should lead to improved predictions of power production. Nevertheless, turbulence measurements over the ocean are far less prevalent than over land due to challenges in instrumental deployment, maintenance, and operation. Atmospheric models such as mesoscale (weather prediction) and large-eddy simulation (LES) models are commonly used in the wind energy industry to assess the spatial variability of a given site. However, the TI derivation from atmospheric models has not been well examined. An algorithm is proposed in this study to realize online calculation of TI in the Weather Research and Forecasting (WRF) model. Simulated TI is divided into two components depending on scale, including sub-grid (parameterized based on turbulence kinetic energy (TKE)) and grid resolved. The sensitivity of sea surface temperature (SST) on simulated TI is also tested. An assessment is performed by using observations collected during a field campaign conducted from February to June 2020 near the Woods Hole Oceanographic Institution Martha's Vineyard Coastal Observatory. Results show that while simulated TKE is generally smaller than the lidar-observed value, wind speed bias is usually small. Overall, this leads to a slight underestimation in sub-grid-scale estimated TI. Improved SST representation subsequently reduces model biases in atmospheric stability as well as wind speed and sub-grid TI near the hub height. Large TI events in conjunction with mesoscale weather systems observed during the studied period pose a challenge to accurately estimating TI from models. Due to notable uncertainty in accurately simulating those events, this suggests summing up sub-grid and resolved TI may not be an ideal solution. Efforts in further improving skills in simulating mesoscale flow and cloud systems are necessary as the next steps.

Iceland wind farm assessment case study and development: An empirical data from wind and wind turbine

This study aimed to apply empirical data to assess wind energy production at the Búrfell site in Iceland based on the E44 turbine model. The empirical data are 5 years of recordings at the site location by the Iceland Metrological office. The wind speed data are measured at a 10 m height from 2017 to 2021. There are two E44 wind turbines test installed on the site. In the previous studies, the wind farm capacity and Levelized cost of energy (LCOE) were reported without investigating the wake loss model and its impacts on LCOE and have an estimation applied. The previous research was based on the two installed wind turbines at the site, which are located in a straight line and perpendicular to the prevailing wind speed. This study applies the Jensen-Katic model to investigate wake loss. Downwind and crosswind ten-rotor diameters and five-rotor diameters are calculated respectively as the best options. Afterward, an appropriate number of wind turbines is suggested for 80MW production. In addition, this study's optimum capacity factor (CF) is 26.08%, which was reported at 37.9% - 38.38% before. On average, the turbines produce less than 30% of their rated power, which has been reported at 38.15% in prior studies. This study presents the LCOE as equal to 0.0659 USD/kWh, which is less than 0.0703 USD/kWh in the previous studies and the LCOE reported by the 2020 LCOE European report. The obtained LCOE in this study is based on the weighted average cost of capital in the energy project by Landsvirkjun, the national power company of Iceland. The obtained result from the model used, which matched the empirical measurements, displays Iceland's best rank for wind energy LCOE metric among European countries. The proposed method provides a vision to use the wake loss model output in deep learning training to predict power production, leading to a sustainable and reliable power grid.

A novel encoding technique to improve non-weather-based models for solar photovoltaic forecasting

Several studies have applied different machine learning (ML) techniques to the area of forecasting solar photovoltaic power production. Most of these studies use weather data as inputs to predict power production; however, there are numerous practical issues with the procurement of this data. This includes the high costs of procurement and lack of backup techniques if communication with weather data services fail. These practical issues are not widely considered yet in the current literature. This study proposes models that do not use weather data as inputs, but rather use past power production data as a more practical substitute to weather-based models. Similar studies have shown satisfactory accuracies, but this study proposes a novel data preprocessing technique—cyclical features encoding—that we hypothesized would boost model accuracy significantly. We used ML techniques to predict power production in a 24-hour time horizon, using input data of the past 48 hours of power production. The Random Forest model offered the best results, with a Pearson Correlation Coefficient of 0.97 (11% higher than previous studies), Mean Absolute Error of 0.0266 (60% better than previous studies), and Root Mean Squared Error of 0.0773 (38% better than previous studies). These results are comparable to state-of-the-art weather models in the field. Our proposed models demonstrate a better, cheaper, and more reliable alternatives to current weather models.

Developing a Neural Network Based Fault Prediction Tool for a Solar Power Plant in Uganda

Solar photovoltaic (PV) systems are one of the fastest growing renewable energy technologies and plenty of research has been and continues to be carried out in this domain. Maximization of solar PV power plant production, efficiency and return on investment can only be achieved by having adequate and effective maintenance systems in place. Of the various maintenance schemes, predictive maintenance is popular for its effectiveness and minimization of resource wastage. Maintenance activities are scheduled based on the real time condition of the system with priority being given to the system components with the highest likelihood of failure. A good predictive maintenance system is based on the premise of being able to anticipate faults before they occur. In this study therefore, a fault prediction tool for a solar plant in Uganda is proposed. The hybrid tool is developed using both feed forward and long short term memory neural networks for power prediction, in conjunction with a mean chart statistical process control tool for final fault prediction. Results from the study demonstrate that the feed forward and long short term memory neural network modules of the proposed tool attain mean absolute errors of 4.2% and 6.9% respectively for power production predictions. The fault prediction capability of the tool is tested under both normal and abnormal operating conditions. Results show that the tool satisfactorily discriminates against the fault and non-fault conditions thereby achieving successful solar PV system fault prediction.

The Jensen wind farm parameterization

Abstract. Wind farm power production is known to be significantly affected by turbine wakes. When mesoscale numerical models are used to predict power production, the turbine wakes cannot be resolved directly because they are sub-grid features, and therefore their effects need to be parameterized. Here we propose a new wind farm parameterization that is based on the Jensen model, a well-known analytical wake model that predicts the expansion and wind speed of an ideal wake. The Jensen parameterization is implemented and inserted into two commonly used atmospheric numerical models: the Weather Research and Forecasting (WRF) model (herein referred to as just “WRF”) and the Model for Prediction Across Scales (MPAS). In addition, the internal variability in wind speed and direction within a wind farm, the wind direction uncertainty, and the superposition of multiple wakes are taken into account with an innovative approach. The proposed approach and parameterization are tested against observational data at two offshore wind farms: Lillgrund (small in size and tightly spaced) and Anholt (large and widely spaced). Results indicate that power production is predicted more accurately with the Jensen wind farm parameterization than with the Fitch wind farm parameterization, which is the only one available in WRF. Power predictions with the Jensen parameterization are similar in WRF and MPAS. The sensitivity to grid resolution is small, and the bias is generally low and negative. In conclusion, we recommend that the Jensen wind farm parameterization be used in WRF and MPAS, especially for coarse resolution, high turbine density, and wind directions aligned with the turbine columns.

Deep ensemble with Neural Networks to model power curve uncertainty

Renewable energy is an essential driver towards tackling the climate crisis. Given the increasing need to ensure sustainability and reduce greenhouse gas emissions, wind energy has become one of the most relevant areas to consider in today’s society. However, it appears challenging to rely on this type of energy because of the uncertain nature of the wind. It is essential to use monitoring tools that can accurately capture the uncertainty of the underlying environmental processes and estimate the generated power to ensure the reliability of wind-based power systems. One of these monitoring tools is the power curve, which captures the relationship between wind speed and expected power production. Many regression techniques have previously been used to predict this power production, including Artificial Neural Networks (ANN). The problem is that they do not capture the uncertainty of the predicted power production. In contrast with the power curve models already implemented when using ANNs, we propose a novel approach based on deep ensembles to predict the mean and model variance on the expected power production. We used three months of Supervisory Control and Data Acquisition data from more than 40 turbines to set up the experiments. The results show that the mean and standard deviation fit the data well. It also appears that the expected mean curve goes through the center of the cloud of original data points, while the standard deviation accurately captures the spread of the data. We conclude that that the deep ensemble approach provides an accurate prediction mechanism to estimate the power production from the wind speed while also offering an uncertainty measure. Therefore, the deep ensemble approach allows us to construct a power curve with uncertainty information.

Prediction of Electric Power Production and Consumption for the CETATEA Building Using Neural Networks

Economic and social development is hardly influenced by electric power production and consumption. In this context of the energy supply pressure, energy production and consumption must be monitored and controlled in an intelligent way. Due to the availability of large data measurements, prediction algorithms based on neural networks are widely used in accurate power prediction. Firstly, the particularity of our work is represented by the size of the dataset consisting of 4 years of continuous real-time data measurements collected from the CETATEA photovoltaic power plant, a research site for renewable energies located in Cluj-Napoca, Romania. Secondly, the high granularity of the dataset with more than 4.2 million unified production and consumption power values recorded every 30 s guarantees the overall prediction accuracy of the system. Performance metrics used to evaluate the prediction accuracy are the mean bias error, the mean square error, the convergence time of the prediction system, the test performance, and the train mean performance. Test results indicate that the predicted unified electric power production and consumption closely resembles the unified electric power measured values.

Upper limb isokinetic muscle strength predicts the performance in cross-country sit-skiing

The double poling (DP) technique in cross-country sit-skiing is primarily considered as an upper-body exercise. The upper limb muscle strength and motion economy are important factors accounting for DP performance in cross-country sit-skiing. The present study investigates how upper limb muscle strength predicts DP performance in cross-country sit-skiing. A total of 19 female non-disabled college students (age 23.2 ± 0.8 years, BMI 20.4 ± 2.2) performed 30-s and 3-min DP performance tests using a sit-skiing ergometer. Isokinetic muscle strength of the shoulder and elbow extensor were measured at the angular velocity of 30°/s, 60°/s, and 120°/s with an ISOMED2000 isokinetic system. A medium correlation was found between DP output power and isokinetic upper limb muscle strength (shoulder strength at all speeds, r = 0.39–0.74, p ≤ 0.1). Multiple regression analyses which were employed to predict power production in the 30-s and 3-min tests showed that shoulder extension strength at 60°/s accounted for 34% of the variation in the 30-s test, and 40% of the variance in the 3-min test. Muscle strength and biomechanical analysis of DP process indicated that upper limb extensor muscle strength and muscle coordination were important factors for the power output generation in sit-skiing DP. These results may use to guide special physical fitness training for paralympic cross-country sit-skiing.

Day-ahead energy production in small hydropower plants: uncertainty-aware forecasts through effective coupling of knowledge and data

Abstract. Motivated by the challenges induced by the so-called Target Model and the associated changes to the current structure of the energy market, we revisit the problem of day-ahead prediction of power production from Small Hydropower Plants (SHPPs) without storage capacity. Using as an example a typical run-of-river SHPP in Western Greece, we test alternative forecasting schemes (from regression-based to machine learning) that take advantage of different levels of information. In this respect, we investigate whether it is preferable to use as predictor the known energy production of previous days, or to predict the day-ahead inflows and next estimate the resulting energy production via simulation. Our analyses indicate that the second approach becomes clearly more advantageous when the expert's knowledge about the hydrological regime and the technical characteristics of the SHPP is incorporated within the model training procedure. Beyond these, we also focus on the predictive uncertainty that characterize such forecasts, with overarching objective to move beyond the standard, yet risky, point forecasting methods, providing a single expected value of power production. Finally, we discuss the use of the proposed forecasting procedure under uncertainty in the real-world electricity market.

Analytical Modeling for Prediction of Horizontal-Axis Wind Turbines Power Generation in Wind Farms Based on an Analytical Wake Model

The development of models that predict power production of wind farms (WFs) by considering the interacting wakes is important; because wakes of the turbines exert a significant influence on power production of turbines, and hence on the layout of wind turbines in WFs. Thus, the purpose of present study was to provide an innovative analytical method for the prediction of power generation of the WFs that have a flat terrain and are consisted of horizontal-axis wind turbines (HAWTs) with the same hub height. The methodology employed utilized an analytical Gaussian model of HAWT wake to develop an analytical model that calculates the effective wind velocity acting on the downstream HAWT(s), which is further used for reading its generated power from the turbine’s catalog; thus, providing the generated power of the WF as the output. The results of presented model were validated by the field measurements data of Horns Rev WF and also were compared to two analytical models for predicting the generated power. The results were compared with two numerical simulations of the literature, and the output data of three commercial software. Moreover, the error analysis revealed that the presented model mostly showed superior accuracy in predicting the field measurements data.

Accurate Demand Forecasting: A Flexible and Balanced Electric Power Production Big Data Virtualization Based on Photovoltaic Power Plant

This paper has tried to execute accurate demand forecasting by utilizing big data visualization and proposes a flexible and balanced electric power production big data virtualization based on a photovoltaic power plant. First of all, this paper has tried to align electricity demand and supply as much as possible using big data. Second, by using big data to predict the supply of new renewable energy, an attempt was made to incorporate new and renewable energy into the current power supply system and to recommend an efficient energy distribution method. The first presented problem that had to be solved was the improvement in the accuracy of the existing electricity demand for forecasting models. This was explained through the relationship between the power demand and the number of specific words in the paper that use crawling by utilizing big data. The next problem arose because the current electricity production and supply system stores the amount of new renewable energy by changing the form of energy that is produced through ESS or that is pumped through water power generation without taking the amount of new renewable energy that is generated from sources such as thermal power, nuclear power, and hydropower into consideration. This occurs due to the difficulty of predicting power production using new renewable energy and the absence of a prediction system, which is a problem due to the inefficiency of changing energy types. Therefore, using game theory, the theoretical foundation of a power demand forecasting model based on big data-based renewable energy production forecasting was prepared.

Time-Dependent Upper Limits to the Performance of Large Wind Farms Due to Mesoscale Atmospheric Response

A prototype of a new physics-based wind resource assessment method is presented, which allows the prediction of upper limits to the performance of large wind farms (including the power loss due to wind farm blockage) in a site-specific and time-dependent manner. The new method combines the two-scale momentum theory with a numerical weather prediction (NWP) model to assess the “extractability” of wind, i.e., how high the wind speed at a given site can be maintained as we increase the number of turbines installed. The new method is applied to an offshore wind farm site in the North Sea to demonstrate that: (1) Only a pair of NWP simulations (one without wind farm and the other with wind farm with an arbitrary level of flow resistance) are required to predict the extractability. (2) The extractability varies significantly from time to time, which may cause more than 30% of change in the upper limit of the performance of medium-to-high-density offshore wind farms. These results suggest the importance of considering not only the natural wind speed but also its extractability in the prediction of (both long- and short-term) power production of large wind farms.