Short-term Power Prediction Research Articles

Output performance prediction of PV modules based on power-law model from manufacturer datasheet

The current–voltage (I–V) equation in the equivalent circuit model of the photovoltaic (PV) module is implicit, and the dependence of model parameters on environmental conditions is uncertain, which causes inconvenience in output performance prediction. In this paper, a novel method based on the power-law model (PLM) is proposed to predict the I–V characteristics and output power of PV modules under varying operating conditions. The relationship between parameters in the PLM and manufacturer datasheet information is established. The irradiance and temperature dependences of shape parameters in PLM are obtained and investigated thoroughly. Due to inherent simplicity and explicit expression of PLM, the proposed method predicts the I–V characteristics and output power without using any iterative process, which reduces the computational complexity. The proposed method is validated by different types PV modules and under a wide range of environmental conditions. Comparing with traditional methods based on a single-diode model, the proposed method has better agreements with experimental results in all irradiance and temperature intervals. The accuracy and effectiveness are verified both in short-term and long-term output power prediction. The proposed method is simple and suitable to predict the actual output properties of PV modules under varying operating conditions.

Short-term PV Power Prediction based on VMD-SE-TCAN Model

<p>Focusing on the accuracy of short-term photovoltaic power prediction, this paper proposes a VMD-SE-TCAN short-term photovoltaic prediction model. First, the Sample Entropy (SE) is used to fuse the different frequency components generated by the Variational Modal Decomposition (VMD) to reduce  the complexity of prediction model and effectively alleviate the under-decomposition or over-decomposition of traditional decomposition algorithm; then the TCAN model based on Temporal Convolution Neural network (TCN) and Attention Mechanism is used to predict each component, the TCN is used to capture temporal dependencies in prediction, and the Attention Mechanism improves the impact of key weather features on different components; finally, the prediction results of each component are superimposed to output photovoltaic prediction power. Through experiments and comparison verification, the combined prediction method effectively improves the accuracy of photovoltaic power prediction, and can better guarantee the reliable operation of the power system.</p> <p> </p>

A short-term prediction model to forecast power of photovoltaic based on MFA-Elman

Due to the low accuracy and reliability as well as slow prediction speed of photovoltaic (PV) power short-term prediction, a short-term PV power prediction method based on MFA-Elman neural network is proposed. K-means mean algorithm is employed firstly in this method to cluster the weather type, and then EEDM is used to decompose the PV output power on the various data obtained by clustering. Finally, each decomposition subsequence is inputted into the MFA-optimized Elman neural network to predicting the PV output power, which solves the randomness of initial weight and threshold and slow training speed of Elman neural network. The simulation results show that the MFA-Elman model has smaller prediction error, higher prediction accuracy and reliability, faster prediction speed, which can reach the requirements of short-term prediction of PV power.

Gaussian Kernel Based SVR Model for Short-Term Photovoltaic MPP Power Prediction

Predicting the power obtained at the output of the photovoltaic (PV) system is fundamental for the optimum use of the PV system. However, it varies at different times of the day depending on intermittent and nonlinear environmental conditions including solar irradiation, temperature and the wind speed, Short-term power prediction is vital in PV systems to reconcile generation and demand in terms of the cost and capacity of the reserve. In this study, a Gaussian kernel based Support Vector Regression (SVR) prediction model using multiple input variables is proposed for estimating the maximum power obtained from using perturb observation method in the different irradiation and the different temperatures for a short-term in the DC-DC boost converter at the PV system. The performance of the kernel-based prediction model depends on the availability of a suitable kernel function that matches the learning objective, since an unsuitable kernel function or hyper parameter tuning results in significantly poor performance. In this study for the first time in the literature both maximum power is obtained at maximum power point and short-term maximum power estimation is made. While evaluating the performance of the suggested model, the PV power data simulated at variable irradiations and variable temperatures for one day in the PV system simulated in MATLAB were used. The maximum power obtained from the simulated system at maximum irradiance was 852.6 W. The accuracy and the performance evaluation of suggested forecasting model were identified utilizing the computing error statistics such as root mean square error (RMSE) and mean square error (MSE) values. MSE and RMSE rates which obtained were 4.5566 * 10−04 and 0.0213 using ANN model. MSE and RMSE rates which obtained were 13.0000 * 10−04 and 0.0362 using SWD-FFNN model. Using SVR model, 1.1548 * 10−05 MSE and 0.0034 RMSE rates were obtained. In the short-term maximum power prediction, SVR gave higher prediction performance according to ANN and SWD-FFNN.

Short-term offshore wind power forecasting - A hybrid model based on Discrete Wavelet Transform (DWT), Seasonal Autoregressive Integrated Moving Average (SARIMA), and deep-learning-based Long Short-Term Memory (LSTM)

Short-term time series wind power predictions are extremely essential for accurate and efficient offshore wind energy evaluation and, in turn, benefit large wind farm operation and maintenance (O&M). However, it is still a challenging task due to the intermittent nature of offshore wind, which significantly increases difficulties in wind power forecasting. In this paper, a novel hybrid model, using unique strengths of Discrete Wavelet Transform (DWT), Seasonal Autoregressive Integrated Moving Average (SARIMA), and Deep-learning-based Long Short-Term Memory (LSTM), was proposed to handle different components in the power time series of an offshore wind turbine in Scotland, where neither the approximation nor the detail was considered as purely nonlinear or linear. Besides, an integrated pre-processing method, incorporating Isolation Forest (IF), resampling, and interpolation was applied for the raw Supervisory Control and Data Acquisition (SCADA) datasets. The proposed DWT-SARIMA-LSTM model provided the highest accuracy among all the observed tests, indicating it could efficiently capture complex times series patterns from offshore wind power.

Ultra Short Term Power Prediction of Offshore Wind Power Based on Support Vector Machine Optimized by Improved Dragonfly Algorithm

In order to improve the prediction effect of ultra short term power of offshorewind power, the prediction model based on support vector machine optimizeddragonfly algorithm is constructed. Based on summary of the predictionmethods of wind power, the support vector machine optimized by dragonflyalgorithm is established. Finally, prediction simulation analysis of offshorewind power is carried out, results show that the proposed prediction model inthis research can effectively improve the computing prediction precision.

All‐factor short‐term photovoltaic output power forecast

Accurately predicting photovoltaic output power is one of the most important basic tasks for the rapid development of the smart grid. The factors that influence photovoltaic output power are not fully considered in the current forecast model. To this end, an all-factor photovoltaic short-term output power prediction model based on improved ensemble empirical mode decomposition (EEMD) and the seeker optimization algorithm (SOA) -BP neural network model is proposed here. First, different similarity algorithms are used in the selection of similar days of different weather types. Second, the factors that affect the PV output power are divided into deterministic parameters and stochastic parameters. The stochastic parameters of similar daily samples are used to constitute a time series, which is decomposed into several stationary signals by EEMD, and then, these signals are trained separately by the SOA-BPNN model. Finally, the final forecast result can be synthesized with the deterministic parameters and the accumulation of the predicted data of individual signals. Compared with the results of different prediction models, the prediction accuracy of the proposed model is much higher.

Short-Term Power Prediction of Building Integrated Photovoltaic (BIPV) System Based on Machine Learning Algorithms

One of the biggest challenges is towards ensuring large-scale integration of photovoltaic systems into buildings. This work is aimed at presenting a building integrated photovoltaic system power prediction concerning the building’s various orientations based on the machine learning data science tools. The proposed prediction methodology comprises a data quality stage, machine learning algorithm, weather clustering assessment, and an accuracy assessment. The results showed that the application of linear regression coefficients to the forecast outputs of the developed photovoltaic power generation neural network improved the PV power generation’s forecast output. The final model resulted from accurate forecasts, exhibiting a root mean square error of 4.42% in NN, 16.86% in QSVM, and 8.76% in TREE. The results are presented with the building facade and roof application such as flat roof, south façade, east façade, and west façade.

A non-intrusive load monitoring approach for very short-term power predictions in commercial buildings

In this study, a new algorithm is developed to extract device profiles in a fully unsupervised manner from three-phases reactive and active aggregate power measurements. The extracted device profiles are then applied to disaggregate the aggregate power measurements by means of particle swarm optimization. Then, a new approach to very short-term power predictions is presented, which makes use of the disaggregation data. For this purpose, a state change forecast is carried out for each device by an artificial neural network and subsequently converted into a power prediction by reconstructing the power profile with respect to the state changes and device profiles. The forecast horizon is 15 min. In order to demonstrate the developed approaches, three-phase reactive and active aggregate power measurements of a multi-tenant commercial building are employed as a case study. The granularity of the data used is 1 s. In total, 52 device profiles are extracted from the aggregate power data. The disaggregation exhibited a highly accurate reconstruction of the measured power with an energy percentage error of approximately 1 %. The indirect power prediction method developed is then applied to the measured power data and outperforms the two persistence forecasts, as well as an artificial neural network designed for 24h-ahead power predictions working in the power domain.

Optimization-oriented adaptive equivalent consumption minimization strategy based on short-term demand power prediction for fuel cell hybrid vehicle

The fuel economy and battery charge sustaining capability are two key criteria for the energy management of a full-power fuel cell hybrid vehicle equipped with small-capacity battery pack. In order to achieve stable battery charge sustenance and near-optimal fuel consumption, this study proposes an optimization-oriented adaptive equivalent consumption minimization strategy (A-ECMS) based on demand power prediction achieved via an iterative predictor. The proposed strategy updates the optimal equivalent factor periodically via local optimization process according to the predicted power to converge the state of charge (SOC) and guarantee fuel economy. The simulation results show that the iterative predictor has considerable accuracy, and the correlation between the predicted data and the real data reaches up to 0.987. The proposed strategy can quickly recover the battery SOC within 40 s in a 500-s driving cycle, which is shorter than existing feedback-oriented A-ECMS. At charge sustaining stage, the proposed strategy maintains the battery SOC around the reference value with an extremely low fluctuation degree of 0.36. The equivalent fossil-fuel consumption of the proposed strategy is 8.003 L/100 km, which is lower than that of existing A-ECMS. Besides, further investigation reveals that the proposed strategy has robust performance against the disturbance of power prediction errors.

Distributed Photovoltaic Short-Term Power Prediction Based On Genetic Algorithm Optimized BP Neural Network

With the rapid development of the world economy, energy demand is increasing.Photovoltaic power generation has the advantages of green, environmental protection and renewable, and the proportion of photovoltaic power generation, a new reusable clean energy in the power generation system, has shown a steady increase;However, the power output of photovoltaic power generation systems is affected by many factors, presenting a high degree of uncertainty and volatility, which brings high difficulties to the large-scale grid-connected operation of photovoltaic power generation.Aiming at the shortcomings of existing photovoltaic power generation forecasting, this paper builds a photovoltaic power station power prediction model based on genetic algorithm and BP neural network algorithm, and uses photovoltaic power station operation examples to verify the validity of the model.By using the global search capability of genetic algorithm to optimize the initial weights and thresholds of BP neural network, the prediction accuracy of photovoltaic power generation is improved, which can provide a reference for photovoltaic power generation prediction engineering practice.

Research on photovoltaic ultra short-term power prediction algorithm based on attention and LSTM

Based on the actual monitoring historical data of photovoltaic power station, combined with the actual engineering demand of photovoltaic microgrid on the user side, the lightweight algorithm of ultra short-term photovoltaic power prediction is studied, which is conducive to improving the operation efficiency and economy of power system. In this paper, the ultra short-term power prediction of photovoltaic power station is carried out by combining the LSTM algorithm with attention mechanism. Firstly, Pearson correlation coefficient method is used to reduce the dimension of the data set. The data with low correlation between weather variables and power to be predicted and historical power are eliminated, and the algorithm model structure is simplified. Then, the attention mechanism is combined with LSTM network to improve the effectiveness of the prediction model for long time series input. The proposed model is trained and compared with the data of a photovoltaic power station. The results show that the model achieves good experimental results in different weather conditions, and can effectively improve the prediction accuracy.

Forecast of photovoltaic power generation based on DBSCAN

The power output of the photovoltaic power generation has prominent intermittent fluctuation characteristics. Large-scale photovoltaic power generation access will bring a specific impact on the safe and stable operation of the power grid. With the increase in the proportion of renewable energy sources such as wind power and photovoltaics, the phenomenon of wind abandonment and light abandonment has further increased. The photovoltaic power generation prediction is one of the critical technologies to solve this problem. It is of outstanding academic and application value to research photovoltaic power generation prediction methods and systems. Therefore, accurately carrying out the power forecast of photovoltaic power plants has become a research hot point in recent years. It is favored by scholars at home and abroad. First, this paper builds a simulation model of the photovoltaic cell based on known theoretical knowledge. Then it uses the density clustering algorithm (DBSCAN) in the clustering algorithm and classifies the original data. Finally, according to a series of problems such as the slow modeling speed of photovoltaic short-term power prediction, the bidirectional LSTM photovoltaic power prediction model, and CNN-GRU photovoltaic power prediction model based on clustering algorithm are proposed. After comparing the two models, it is concluded that the bidirectional LSTM prediction model is more accurate.

Research on power prediction of photovoltaic power station based on similar hour and LM-BP neural network

Aiming to solve the problem of low precision of traditional photovoltaic power forecast method under abrupt weather conditions. In this paper, a high-precision photovoltaic power prediction method based on similarity time and LM-BP neural network is proposed. Firstly, the factors affecting the output power of photovoltaic power station are analyzed, and the short-term output power model of photovoltaic power station is established based on similar day and LM-BP neural network. Then, from the perspective of model training efficiency and prediction accuracy, the deficiencies in the short-term power prediction of photovoltaic power stations based on similar days and LM-BP algorithm are analyzed. Secondly, the prediction model of LM-BP neural network based on similar hours is established. Finally, Jiaxing photovoltaic power station is taken as an example for simulation verification. The simulation results show that the proposed method has high accuracy in predicting photovoltaic power under abrupt weather conditions.

Hybrid Improved Bird Swarm Algorithm with Extreme Learning Machine for Short-Term Power Prediction in Photovoltaic Power Generation System.

When a photovoltaic (PV) system is connected to the electric power grid, the power system reliability may be exposed to a threat due to its inherent randomness and volatility. Consequently, predicting PV power generation becomes necessary for reasonable power distribution scheduling. A hybrid model based on an improved bird swarm algorithm (IBSA) with extreme learning machine (ELM) algorithm, i.e., IBSAELM, was developed in this study for better prediction of the short-term PV output power. The IBSA model was initially used to optimize the hidden layer threshold and input weight of the ELM model. Further, the obtained optimal parameters were input into the ELM model for predicting short-term PV power. The results revealed that the IBSAELM model is superior in terms of the prediction accuracy compared to existing methods, such as support vector machine (SVM), back propagation neural network (BP), Gaussian process regression (GPR), and bird swarm algorithm with extreme learning machine (BSAELM) models. Accordingly, it achieved great benefits in terms of the utilization efficiency of whole power generation. Furthermore, the stability of the power grid was well maintained, resulting in balanced power generation, transmission, and electricity consumption.

Wind power short-term prediction over mountain area using a high-resolution WRF model

Accurate wind power prediction are crucial for power-grid integration and load balancing, as well as the safe and stable operation of the power grid. In this study, the relationship between the wind speed and wind power over mountain area is firstly investigated using the observations in Hunan Baiguoshan Mountain, and the fitting equation is proposed to predict the wind power with wind speed. Using the simulation of the WRF model with a 3-kilometer horizontal resolution, its prediction performance for short-term wind power is further analyzed. The results show that a sixth power relationship exists between wind speeds and wind powers over the mountain area. Also, when the wind speed reaches up to about 9.5 m/s (half of the cut-out wind speeds), the wind power is almost up to its rated power (2200 KW). The evolution characteristics of the wind powers predicted by the WRF model resemble that in observations, but the predicted wind powers are larger than that as observations in most time, which results from the overestimated predicted wind speeds like that in observations.

Short-term PV power prediction considering the influence of aerosol

To improve the accuracy of photovoltaic power prediction under haze weather, a short-term PV power prediction method combining the PM2.5 forecast and SVR model is proposed. The WRF-CHEM air quality model is used to realize the simulation calculation of PM2.5 concentration, combined with the historical output data of PV power plants, and based on the SVR machine learning model to build a direct mapping relationship model between multiple meteorological elements and PV output. A forecast experiment was carried out for a certain area in North China, and the results showed that the proposed method can effectively improve the accuracy of PV power forecasting under haze weather conditions, thereby providing strong support for power grid dispatch and operation.

Deep Learning Forecaster-Based Controller for SVC: Wind Farm Flicker Mitigation

One of the major challenges in grid-connected wind farms is the flicker emission caused by the reactive power extremely short time variations. A general used solution is installing a static volt-ampere reactive (VAR) compensator (SVC) as the most cost-saving device, which still holds the risk of incomplete reactive power compensation subject to the operating delay time. To this end, an accurate extreme short-term reactive power forecasting structure is essential. Although there are many studies related to the forecasting of wind power in long, medium, and short terms, however, the extremely short-term reactive power prediction of the wind farms is only addressed in few studies. The main aim in this article is to develop a method based on the convolutional neural network (CNN) to directly learn nonstationary and complex features from the raw wind farm reactive power time series and to contribute a predictive controller to mitigate the voltage flicker through an SVC connected to the wind farm. However, high sensitivity of typical mean-squared-based loss functions might lead to considerable errors in the forecasting. To resolve this issue, a modified loss function is proposed to enhance the performance and, consequently, an improved CNN is presented. The evaluation of the proposed method is based on the actual recorded data of a wind farm in Manjil, Iran. The data are directly used in the modeling of the wind farm. A current source, which is based on the actual records its amplitude and phase angle change every 0.01 s, is utilized to model the wind farm. The numerical results in terms of flicker sensation and short-term flicker perceptibility (Pst) measurement are used to verify the performance of the proposed method through comparison with the wind farm performance without SVC and SVC with a common control system.

Turbine-specific short-term wind speed forecasting considering within-farm wind field dependencies and fluctuations

The unprecedented scale and sophistication of wind turbine technologies call for wind forecasts of high spatial resolution, i.e. turbine-tailored forecasts, to inform several operational decisions at the turbine level. Towards that, this paper is concerned with leveraging the hub-height measurements collected from a fleet of turbines on a farm to make turbine-specific short-term wind speed and power predictions. We find that the wind propagation across a dense grid of turbines induces strong spatial and temporal dependencies in the within-farm wind field, but also gives rise to high-frequency high-magnitude fluctuations which may compromise the predictive accuracy of several data-driven forecasting methods. To capture both aspects, we propose to model the total variability in the within-farm wind speed field as a combination of two independent stochastic process terms. The first term reconstructs and extrapolates the wind speed field by learning the complex spatio-temporal dependence structure using hub-height turbine-level data. The second term accounts for high-frequency high-magnitude fluctuations that are not informed by near-term spatio-temporal dependencies. The two terms are coupled to make probabilistic wind speed forecasts at each turbine, which are then translated into turbine-specific power predictions via wind power curves. Evaluation on more than 3,000,000 data points from a wind farm dataset provides a strong empirical evidence in favor of the proposed method’s forecasting accuracy. On average, our proposed method achieves 9% accuracy improvement relative to persistence forecasts, and 7–9% relative to a set of widely recognized forecasting methods such as autoregressive-based models and Gaussian Processes.

RETRACTED ARTICLE: Research on photovoltaic output power short term prediction method based on machine learning

RETRACTED ARTICLE: Research on photovoltaic output power short term prediction method based on machine learning