Prediction Of Power Output Research Articles

An Ultra‐Short‐Term Wind Power Prediction Method Based on Spatiotemporal Characteristics Fusion

ABSTRACTAiming at the problem that the existing ultra‐short‐term wind power prediction methods lack consideration of the spatial correlation characteristics of wind farms, resulting in insufficient prediction accuracy, an ultra‐short‐term wind power prediction method based on spatiotemporal characteristics fusion is proposed in this article. First, the fluctuation difference of the time window of wind power is input into the K‐means clustering algorithm to cluster wind farms into several clusters based on power fluctuation similarity. Then, the principal component analysis algorithm is used to reduce the dimensionality of numerical weather prediction data combinations in different regions to reduce the impact of redundant information on modeling accuracy. Finally, a convolutional long‐short‐term memory neural network is designed to extract spatiotemporal features of wind power data and output prediction results. The experimental verification on 18 wind farms in a province in China shows that the proposed wind power prediction method has an average root mean square error of only 0.1257 and has certain applicability.

Novel optimized models to enhance performance forecasting of grid-connected PERC PV string operating under semi-arid climate conditions

This study focuses on modeling and forecasting the performance of a grid-connected photovoltaic (PV) string, aiming to enhance the accuracy of output power prediction, which is crucial for the effective management and stability of the electrical grid. While previous research has extensively examined PV modules, there is a notable lack of studies specifically targeting PV string behaviors, especially in harsh climates prevailing in semi-arid regions. This study addresses this gap by investigating the modeling and performance prediction of a Passivated Emitter Rear Cell (PERC) PV string under semi-arid climate conditions using analytical and predictive approaches based on both implicit and explicit models as Single Diode Model (SDM), Das Model (DM), Boutana et al. Model (BM) and Power Law Model (PLM). New mathematical models of DM and BM shape parameters versus temperature and irradiance along with novel analytical formulas giving DM shape parameters as a function of PV metrics have been introduced. The proposed approaches are validated using real measurements of a PERC PV string incorporating 12 JKM405M-72H PV modules operating at Green Energy Park (GEP) research facility in Morocco. The reliability of these approaches is assessed by comparing I-V curves, P-V curves and peak power generated and predicted by SDM, DM, BM and PLM to actual measurements. The comparison reveals that generated and predicted outcomes align well with measured data, with an average value of Normalized Root Mean Square Error (NRMSE) not exceeding 4.16 % for all models throughout the day. The findings indicate that the proposed approaches effectively predict the performance of the PERC PV string, accounting for climatic influences and providing insights into optimizing PV system performance, thereby contributing to improved grid stability in semi-arid regions.

The impact of effective fractured rock mass properties on development of flow channeling in enhanced geothermal systems

Controlling the distribution of working fluid in an Enhanced Geothermal System (EGS) is crucial to prevent early thermal breakthrough and sustain the initial heat drainage area. Over time, working fluid flow may localize to a small area if fracture permeability increases non-uniformly, and efforts are not made to control the flow distribution. This scenario can potentially lead to mechanical reopening of hydraulic fractures, flow channeling, and thermal short-circuiting. However, current models neglect the nonlinear and inelastic deformation of fractured rock masses on EGS coupled thermo-mechanical response. The objective of this work is to estimate and predict the likelihood of thermal short-circuiting by flow-channeling in an EGS reservoir composed of rock and compliant natural fractures. We utilize three dimensional numerical solutions with reservoir properties based on effective medium theory of fractured rocks to achieve this objective. The results show that presence of natural fractures considerably changes the EGS response to thermal destressing, compared to linear poroelastic models. Flow channeling and short-circuiting, resulting in early thermal breakthrough, are more likely to occur in sparsely fractured reservoirs with stiff and strong natural fractures. Conversely, short-circuiting is unlikely in densely fractured reservoirs where thermal strains are absorbed by natural fracture opening and shear sliding rather than by rock matrix contraction. Estimation of natural fracture density is crucial in long-term forecasting and prediction of EGS power output. Accurate fracture characterization could decisively impact the fate of a project.

Structural multistability for multi-speed wind energy harvesting from vortex-induced vibrations

Abstract Piezoelectric energy harvesters utilizing vortex-induced vibrations (VIV) have been extensively studied for converting wind energy into usable power for microelectronics. In this work, we explore the use of structural bistability to increase the range of flow speeds over which energy can be harvested without the need for complicated assemblies. We propose a harvester system featuring a piezoelectric transducer bonded to a cantilevered bistable composite laminate, which has two distinct equilibrium shapes at room temperature. To enhance the VIV, we attach a cylindrical bluff body to the free edge of the harvester. The structure’s inherent bistability allows for high power generation at two different flow speeds, contrasting with the single synchronization region typical of linear piezoelectric harvesters. We develop a reduced-order model to predict power output across varying flow speeds and validate these predictions through wind tunnel experiments, showing good agreement. Furthermore, we conduct a parametric study to optimize the model parameters for maximum power output. Our results demonstrate that the bistable harvester can generate up to 4.5 mW of power over a wind speed range of 9.3 m s−1–11.7 m s−1, outperforming the limited speed range of traditional linear VIV-based harvesters. This work underscores the potential to design VIV-based energy harvesters capable of operating efficiently across multiple flow speed ranges using a single structure with its dual stable configurations.

Towards a high resolution simulation framework for building integrated photovoltaics under partial shading in urban environments

This paper presents a framework for modeling the performance of photovoltaic (PV) systems, which is intended to be used in the design, optimization, and deployment of PV systems in urban environments. Urban PV and building integrated PVs (BIPV) are a crucial component of the transition to a low-carbon society. However, existing frameworks for simulating PV systems accurately characterize PV under partial shading, leading to performance gaps with systems deployed in environments such as cities where shading is more common than in large open fields. In this paper, we extend a framework from literature for modeling parametric BIPV arrays using a power model that characterizes IV-curves at the PV cell. We describe the framework and perform a validation against a measured rooftop PV array dataset. We then evaluate the extended framework to other PV modeling and simulation frameworks found in the literature through the simulation of a simplified facade under various levels of shading. Results show that the extended framework is necessary to account for partial shading, but there may be use cases when lower resolution frameworks may be just as accurate. Amongst the frameworks a range of predicted power outputs under the shading conditions was observed; from 6% to 846% with a mean of 162% from the other frameworks relative to the proposed framework. The range is due to the extensive set of conditions tested, where it was observed that in conditions where shading is present but sparse, there is a larger range of predicted output from the various frameworks. Whereas in conditions where shading coverage is high this range is lower. Lastly, we demonstrate the use of the framework on three arrays. Again it was observed that homogeneous shading conditions may not require the level of resolution provided by the proposed framework, while heterogeneous could benefit the most from the approach. This indicates, and is discussed in the paper, that modelers should consider the shading conditions present when deciding on which framework to apply.

Heat recovery integration in a hybrid geothermal-based system producing power and heating using machine learning approach to maximize outputs

This study offers an in-depth thermodynamic analysis and optimization of an integrated renewable energy system that merges a double-flash geothermal system with a transcritical carbon dioxide Rankine cycle, utilizing machine learning algorithms. The innovative design aims to maximize the concurrent generation of heat and electricity, ultimately benefiting environmental sustainability and energy security. By employing regression machine learning algorithms, the research evaluates and enhances system performance, achieving remarkable R-squared accuracy levels of 98.86 % for heating output and 99.89 % for power output predictions. The thermodynamic modeling, which has been validated against recognized benchmarks, confirms the accuracy of the system's design. Optimization findings indicate that operating pressures between 840 and 870 kPa and pressure ratios of 1.56–1.60 deliver optimal outputs, with power production between 2582 and 2585 kW and heating output ranging from 12260 to 12280 kW. The system reaches its maximum performance at a pressure of 850 kPa and a pressure ratio of 1.57, resulting in a power output of 2583.97 kW and a heating output of 12279.3 kW. These results highlight the potential of combining advanced thermodynamic systems with machine learning methodologies to improve the efficiency and effectiveness of renewable energy sources.

Optimization strategies for bifacial floating photovoltaic systems on different water bodies: A 10E performance evaluation

This study investigates the performance and optimization of standalone floating bifacial solar photovoltaic systems through a comprehensive 10E analysis, covering energy, exergy, economic, environmental, energo-economic, exergo-economic, enviro-economic, energo-environmental, exergo-environmental, energy payback time, and embodied energy factors. The research evaluates energy efficiency, economic feasibility, and environmental impact using various water coolants. Fresh water emerged as the best performer, delivering the highest output power (399 W), final yield (371.94 W), and performance ratio (59.082). Capacity utilization factors were comparable, with fresh water (0.517) and black water (0.516) leading. Fresh water also showed the lowest energy loss (−365.64) and highest exergy efficiency (32.10%). It achieved the lowest levelized cost of energy at 3.39 $/MWh and the highest enviro-economic parameter (243.497). The exergo-environmental analysis demonstrated consistent efficiency, with energy payback time ranging from 37.28 to 37.36, and the embodied energy for the panels was 2840.67 units. Optimization techniques such as random forest, gradient boosting, XGBoost gain, neural network, and response surface methodology were used to identify optimal panel heights, water depths, and azimuth angles. Fresh water was identified as the most effective coolant, balancing high energy efficiency, minimal energy loss, and environmental sustainability. Response surface methodology proved to be the most accurate method for predicting power output and bifacial gain. This study provides valuable insights into improving the efficiency and sustainability of floating bifacial photovoltaic systems under various environmental conditions.

Ultra-Short-Term Photovoltaic Power Prediction by NRGA-BiLSTM Considering Seasonality and Periodicity of Data

Photovoltaic (PV) power generation is highly stochastic and intermittent, which poses a challenge to the planning and operation of existing power systems. To enhance the accuracy of PV power prediction and ensure the safe operation of the power system, a novel approach based on seasonal division and a periodic attention mechanism (PAM) for PV power prediction is proposed. First, the dataset is divided into three components of trend, period, and residual under fuzzy c-means clustering (FCM) and the seasonal decomposition (SD) method according to four seasons. Three independent bidirectional long short-term memory (BiLTSM) networks are constructed for these subsequences. Then, the network is optimized using the improved Newton–Raphson genetic algorithm (NRGA), and the innovative PAM is added to focus on the periodic characteristics of the data. Finally, the results of each component are summarized to obtain the final prediction results. A case study of the Australian DKASC Alice Spring PV power plant dataset demonstrates the performance of the proposed approach. Compared with other paper models, the MAE, RMSE, and MAPE performance evaluation indexes show that the proposed approach has excellent performance in predicting output power accuracy and stability.

Data-Driven Power Prediction for Proton Exchange Membrane Fuel Cell Reactor Systems.

Enhancing high-performance proton exchange membrane fuel cell (PEMFC) technology is crucial for the widespread adoption of hydrogen energy, a leading renewable resource. In this research, we introduce an innovative and cost-effective data-driven approach using the BP-AdaBoost algorithm to accurately predict the power output of hydrogen fuel cell stacks. The algorithm's effectiveness was validated with experimental data obtained from an advanced fuel cell testing platform, where the predicted power outputs closely matched the actual results. Our findings demonstrate that the BP-AdaBoost algorithm achieved lower RMSE and MAE, along with higher R2, compared to other models, such as Partial Least Squares Regression (PLS), Support Vector Machine (SVM), and back propagation (BP) neural networks, when predicting power output for electric stacks of the same type. However, the algorithm's performance decreased when applied to electric stacks with varying material compositions, highlighting the need for more sophisticated models to handle such diversity. These results underscore the potential of the BP-AdaBoost algorithm to improve PEMFC efficiency while also emphasizing the necessity for further research to develop models capable of accurately predicting power output across different types of PEMFC stacks.

Short-term PV energy yield predictions within city neighborhoods for optimum grid management

The global shift towards the exploitation of renewable energy sources is a key aspect in the transition to carbon-free societies. Although harnessing of PV energy disclosed the numerous environmental and economic benefits of PV systems, the ever-increasing share of PV systems in power generation has revealed their adverse effect on the electricity grid due to their dependence on weather conditions. Uncertain environmental parameters, especially cloud cover, can adversely affect the stability of the energy generated by a PV system, causing dynamic changes in PV power generation in a given future time period. Therefore, accurate predictions of PV power output are a major challenge, as they can contribute to the optimal management and flexibility of the power grid, and to the improvement of the operating efficiency of PV power stations, thus enabling the development of flexible green power electricity grids across cities. In addition, precise forecasting can provide system/grid operators with information needed for efficient capacity management and scheduling and ensure grid stability.Most forecasting models provide promising results in terms of error performance; however, they need weather variables or satellite information as inputs that make prognosis significantly challenging. Hence, the current work utilizes a data-driven method for predicting PV power generation from distributed PV systems. The spatio-temporal model Sparse Gaussian Conditional Random Fields was considered. Although this model was used extensively for wind forecasting, in this work, it was used for solar PV power output predictions. Moreover, the effect of varying spatial distribution for aggregated multiple PV systems was investigated on the forecast model performance. The main objective was to provide the solar PV energy sector with important information for building a smart grid for cities or regions.Continuous data from a grid-connected dense network of 21 PV systems was used within a region of 38.485 km2 in Nicosia, Cyprus. It should be noted that the test set includes data only from winter months. The results showed that the predicted power output signal responds to the fluctuation and follows the trend of the actual power signal, even on days with large variations in actual PV power. The average nRMSE of all PV systems in each dataset studied ranges from 0.119 to 0.146, proving that the proposed method delivered significant results that can be compared with existing results from similar studies. Overall, the proposed method can provide important information that can assist in decision-making for the management, optimization, and visualization of grids across cities.

Prediction of electricity load generated by Combined Cycle Power Plants using integration of machine learning methods and HGS algorithm

Combined Cycle Power Plants offer an efficient and environmentally friendly means of electricity generation, albeit with challenges in accurately predicting power output. This study proposes a hybrid model to forecast the total electrical power load in these plants by leveraging key input variables. Five machine learning algorithms, including Categorical Boosting, Histogram-based Gradient Boosting Regression, Extreme Gradient Boosting Regression, Light Gradient Boosting Machine, and Support Vector Regression, were employed, with their predictions optimized using the Hunger Games Search algorithm. This integration resulted in five hybrid models, with the combination of Hunger Games Search and Categorical Boosting emerging as the most effective, demonstrating superior performance in test datasets with an R2 value of 0.9735 and a mean absolute error of 2.05525. The Hunger Games Search algorithm enhanced prediction accuracy by fine-tuning core algorithm parameters. Comprehensive case analysis and metric evaluation underscored the efficacy of the proposed model for hourly power load forecasting in Combined Cycle Power Plants, although the hybrid model combining Hunger Games Search and Support Vector Regression exhibited comparatively poorer performance in test datasets with R2 of 0.9551 and a mean absolute error of 2.71211. The recommendation to integrate Categorical Boosting with the Hunger Games Search algorithm stands as a robust strategy for enhancing power load prediction in these power plants, promising greater operational efficiency and reliability in electricity generation.

Deep learning-enabled integration of renewable energy sources through photovoltaics in buildings

Installing photovoltaic (PV) systems in buildings is one of the most effective strategies for achieving sustainable energy goals and reducing carbon emissions. However, the requirement for efficient energy management, the fluctuating energy demands, and the intermittent nature of solar power are a few of the obstacles to the seamless integration of PV systems into buildings. These complexities surpass the capabilities of rule-based systems, necessitating innovative solutions. The research proposes a deep learning-based optimal energy management system designed specifically for home micro-grids that incorporate PV systems with battery energy storage, Enhanced Long Short-Term Memory (LSTM)-Based Optimal Home Micro-Grid Energy Management (OHM-GEM). Integrating an improved type of LSTM neural network called LSTM into the energy management system improves the reliability of PV power output predictions. The dependability of PV power production forecasts is increased by including a refined version of the LSTM neural network in the energy management system. The efficiency of the OHM-GEM system in maximizing PV system integration into buildings is shown by the authors using simulated data. With considerable gains in energy efficiency, cost savings, and decreased reliance on non-renewable energy sources, the results highlight the possibility of this approach to forward sustainable energy practices.

Output power prediction of stratospheric airship solar array based on surrogate model under global wind field

Stratospheric airships are lighter-than-air vehicles capable of continuous flying for months. The energy balance of the airship is the key to long-duration flights. The stratospheric airship is entirely powered by the solar array. It is necessary to accurately predict the output power of the array for any flight state. Because of the uneven solar radiation received by the solar array, the traditional model based on components has a slow simulation speed. In this study, a data-driven surrogate modeling approach for prediction the output power of the solar array is proposed. The surrogate model is trained using the samples obtained from the high-accuracy simulation model. By using the input parameter preprocessor, the accuracy of the surrogate model in predicting the output power of the solar array is improved to 98.65%. In addition, the predictive speed of the surrogate model is ten million times faster than the traditional simulation model. Finally, the surrogate model is used to predict the energy balance of stratospheric airships flying throughout the year under actual global wind fields.

Short term hydropower scheduling considering cumulative forecasting deviation of wind and photovoltaic power

The cumulative forecasting errors of wind and photovoltaic (PV) power pose serious challenges to the short-term scheduling of hydropower stations that connected to the same regional power grid. The variable and intermittent nature of wind and PV power necessitate a flexible power source to achieve power balance. Consequently, hydropower, with its flexible adjustment capability, plays a crucial role in supporting the consumption of wind and PV power stations in short-term scheduling. Meanwhile, to address the grid's peak shaving requirement, the scheduling of cascaded hydropower system becomes increasingly complex. Given the limitations of current wind and PV power output prediction technologies, achieving precise forecast remains challenging. In practice, the actual outputs of wind and PV power stations often diverge considerably from predicted values over multiple consecutive periods. This variability may result in the underutilization of wind and PV power if the reserve capacity of hydropower stations is insufficient. Therefore, it is essential to account for the cumulative deviation in electricity from wind and PV sources. By computing probability distribution and establishing boundary condition of the cumulative deviation, the operation of the cascaded hydropower system can be controlled more effectively, thus enhancing the stability of a multi-energy complementary system. This study employs Gaussian Mixture Model (GMM) to characterize the probability distributions of cumulative electricity deviation across multiple periods of wind and PV power and takes it as restriction to regulate the operation of hydropower stations. In order to balance the deviation electricity, the downward electricity reserve, upward electricity reserve and the storage energy of the cascaded reservoirs are introduced as restrictive conditions. The proposed Hydro-wind-PV peak shaving model is then applied to the short-term optimal scheduling of the Lancang River cascaded hydropower system to validate the feasibility and efficacy.

Deep learning-based evaluation of photovoltaic power generation

Photovoltaic (PV) power generation has emerged as a rapidly growing renewable energy source. However, the PV system output’s intermittent and weather-dependent nature poses challenges when integrating with the power grid. These challenges manifest as critical issues, including voltage fluctuations, harmonic distortion, and current deviation, making it difficult to accurately predict grid conditions. Moreover, the spatial variability caused by PV system intermittency further complicates the situation. To address these challenges and ensure efficient grid integration, this paper proposes a comprehensive approach encompassing deep learning-based state prediction of PV power output. The paper introduces the utilization of a long short-term memory (LSTM) model, a type of deep learning architecture, for learning patterns from historical PV power generation data and weather forecasts. The LSTM model enables accurate predictions for effective grid management by capturing long-term dependencies in PV power generation data. Real-world PV power generation data was employed to evaluate the proposed approach. The results demonstrated the significant improvement in PV power generation prediction accuracy achieved by the LSTM model compared to traditional methods. The proposed approach offers a promising solution for addressing the challenges associated with PV system integration into the power grid. It enables enhanced grid planning, resource allocation, and protection measures to accommodate the increasing penetration of solar energy harnessed through PV systems and related power electronics interfaces.

A Parallel Prediction Model for Photovoltaic Power Using Multi-Level Attention and Similar Day Clustering

Photovoltaic (PV) power generation is significantly impacted by environmental factors that exhibit substantial uncertainty and volatility, posing a critical challenge for accurate PV power prediction in power system management. To address this, a parallel model is proposed for PV short-term prediction utilizing a multi-level attention mechanism. Firstly, gray relation analysis (GRA) and an improved ISODATA algorithm are used to select a dataset of similar days with comparable meteorological characteristics to the forecast day. A transformer encoder layer with multi-head attention is then used to extract long-term dependency features. Concurrently, BiGRU, optimized with a Global Attention network, is used to capture global temporal features. Feature fusion is performed using Cross Attention, calculating attention weights to emphasize significant features and enhancing feature integration. Finally, high-precision predictions are achieved through a fully connected layer. Utilizing historical PV power generation data to predict power output under various weather conditions, the proposed model demonstrates superior performance across all three climate types compared to other models, achieving more reliable predictions.

Day-Ahead Prediction of PV Power Output: A One-Year Case Study at Changwon in South Korea

Day-Ahead Prediction of PV Power Output: A One-Year Case Study at Changwon in South Korea

Performance Correction and Parameters Identification Considering Non-Uniform Electric Field in Cantilevered Piezoelectric Energy Harvesters.

In the current electromechanical model of cantilevered piezoelectric energy harvesters, the assumption of uniform electric field strength within the piezoelectric layer is commonly made. This uniform electric field assumption seems reasonable since the piezoelectric layer looks like a parallel-plate capacitor. However, for a piezoelectric bender, the strain distribution along the thickness direction is not uniform, which means the internal electric field generated by the spontaneous polarization cannot be uniform. In the present study, a non-uniform electric field in the piezoelectric layer is resolved using electrostatic equilibrium equations. Based on these, the traditional distributed parameter electromechanical model is corrected and simplified to a practical single mode one. Compared with a traditional model adopting a uniform electric field, the bending stiffness term involved in the electromechanical governing equations is explicitly corrected. Through comparisons of predicted power output with two-dimensional finite element analysis, the results show that the present model can better predict the power output performance compared with the traditional model. It is found that the relative corrections to traditional model have nothing to do with the absolute dimensions of the harvesters, but only relate to three dimensionless parameters, i.e., the ratio of the elastic layer's to the piezoelectric layer's thickness; the ratio of the elastic modulus of the elastic layer to the piezoelectric layer; and the piezoelectric materials' electromechanical coupling coefficient squared, k312. It is also found that the upper-limit relative corrections are only related to k312, i.e., the higher k312 is, the larger the upper-limit relative corrections will be. For a PZT-5 unimorph harvester, the relative corrections of bending stiffness and corresponding resonant frequency are up to 17.8% and 8.5%, respectively. An inverse problem to identify the material parameters based on experimentally obtained power output performance is also investigated. The results show that the accuracy of material parameters identification is improved when considering a non-uniform electric field.

Joint Modeling of Wind Speed and Power via a Nonparametric Approach

Power output from wind turbines is influenced by wind speed, but the traditional theoretical power curve approach introduces uncertainty into wind energy forecasting models. This is because it assumes a consistent power output for a given wind speed. To address this issue, a new nonparametric method has been proposed. It uses K-means clustering to estimate wind speed intervals, applies kernel density estimation (KDE) to establish the probability density function (PDF) for each interval and employs Monte Carlo simulation to predict power output based on the PDF. The method was tested using data from the MERRA-2 database, covering five wind farms in Brazil. The results showed that the new method outperformed the conventional estimation technique, improving estimates by an average of 47 to 49%. This study contributes by (i) proposing a new nonparametric method for modeling the relationship between wind speed and power; (ii) emphasizing the superiority of probabilistic modeling in capturing the natural variability in wind generation; (iii) demonstrating the benefits of temporally segregating data; (iv) highlighting how different wind farms within the same region can have distinct generation profiles due to environmental and technical factors; and (v) underscoring the significance and reliability of the data provided by the MERRA-2 database.

Short-Term Photovoltaic System Output Power Prediction Based on Integrated Deep Learning Algorithms in the Clean Energy Sector

Photovoltaic power generation system plays an important role in renewable energy. Therefore, accurately predicting the short-term output power of photovoltaic system has become a key challenge for real-time power grid management. This study focuses on Yingli's green energy photovoltaic system, and uses the convolution neural network and long-term and short-term memory network fusion model (CNN-LSTM) to predict the short-term power. The model integrates CNN's data feature extraction and LSTM's time series prediction ability, showing high accuracy and stability. The experimental results show that CNN-LSTM model has a low mean and variance of prediction error, and the prediction is stable and reliable, and it is consistent in different scenarios. This provides theoretical support for the output power prediction of photovoltaic system based on deep learning.