Forecasts Of Power Production Research Articles

Advanced simulation-based predictive modelling for solar irradiance sensor farms

ABSTRACT The need for accurate solar power forecasting is critical for grid stability as solar energy becomes more prevalent. This paper presents a new framework called Cloud-based Analysis and Integration for Data Efficiency (CAIDE) for real-time monitoring and forecasting of solar irradiance in sensor farms. CAIDE can handle multiple sensor farms, enhance predictive models in real-time, and is built on Model Based Systems Engineering (MBSE) and Internet of Things (IoT) technologies. It can correct its forecasts, ensuring they stay current, and operates on various architectures, ensuring scalability. Tested on multiple sensor farms, CAIDE proved to be scalable and improved the initial accuracy of solar power production forecasts in real-time. This framework is significant for solar plant deployment and the advancement of renewable energy.

A Dependability Neural Network Approach for Short-Term Production Estimation of a Wind Power Plant

This paper presents a novel approach to estimating short-term production of wind farms, which are made up of numerous turbine generators. It harnesses the power of big data through a blend of data-driven and model-based methods. Specifically, it combines an Artificial Neural Network (ANN) for immediate future predictions of wind turbine power output with a stochastic model for dependability, using Hybrid Reliability Block Diagrams. A thorough state-of-the-art review has been conducted in order to demonstrate the applicability of an ANN for non-linear stochastic problems of energy or power forecast estimation. The study leverages an innovative cluster analysis to group wind turbines and reduce the computational effort of the ANN, with a dependability model that improves the accuracy of the data-driven output estimation. Therefore, the main novelty is the employment of a hybrid model that combines an ANN with a dependability stochastic model that accounts for the realistic operational scenarios of wind turbines, including their susceptibility to random shutdowns This approach marks a significant advancement in the field, introducing a methodology which can aid the design and the power production forecast. The research has been applied to a case study of a 24 MW wind farm located in the south of Italy, characterized by 28 turbines. The findings demonstrate that the integrated model significantly enhances short-term wind-energy production estimation, achieving a 480% improvement in accuracy over the solo-clustering approach.

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.

Recent developments in multivariate wind and solar power forecasting

AbstractThe intermittency of renewable energy sources, such as wind and solar, means that they require reliable and accurate forecasts to integrate properly into energy systems. This review introduces and examines a selection of state‐of‐the‐art methods that are applied for multivariate forecasting of wind and solar power production. Methods such as conditional parametric and combined forecasting already see wide use in practice, both commercially and scientifically. In the context of multivariate forecasting, it is vital to model the dependence between forecasts correctly. In recent years, reconciliation of forecasts to ensure coherency across spatial and temporal aggregation levels has shown great promise in increasing the accuracy of renewable energy forecasts. We introduce the methodology used for forecast reconciliation and review some recent applications for wind and solar power forecasting. Many forecasters are beginning to see the benefit of the greater information provided by probabilistic forecasts. We highlight stochastic differential equations as a method for probabilistic forecasting, which can also model the dependence structure. Lastly, we discuss forecast evaluation and how choosing a proper approach to evaluation is vital to avoid misrepresenting forecasts.This article is categorized under: Climate and Environment > Net Zero Planning and Decarbonization Sustainable Energy > Solar Energy Sustainable Energy > Wind Energy

Power production forecast for distributed wind energy systems using support vector regression

AbstractDue to the inherent intermittency in wind power production, reliable short‐term wind power production forecasting has become essential for the efficient grid and market integration of wind energy. The current wind power production forecasting schemes are predominantly developed for wind farms. With the rapid growth in the microgrid sector and the increasing number of wind turbines integrated with these local grids, power production forecasting schemes are becoming essential for distributed wind energy systems as well. This paper proposes a power production forecasting scheme developed explicitly for distributed wind energy projects. The proposed system integrates two submodels based on support vector regression: one for downscaling the wind speed predictions to the hub coordinates of the turbine and the other for predicting the site‐specific performance of the turbine under this wind condition. The forecasting horizons considered are the hour ahead (t + 1) and the day ahead (t + 36), which align with the Nord pool's energy market requirements. For the day‐ahead scheme, a multistep recursive approach is adopted. The accuracy and adaptability of the proposed forecasting scheme are demonstrated in the case of a distributed wind turbine.

Experiments and Comparison of Digital Twinning of Photovoltaic Panels by Machine Learning Models and a Cyber-Physical Model in Modelica

We present two approaches for digital twinning in the context of the forecast of power production by photovoltaic panels. We employ two digital models that are complementary: the first one is a cyber-physical system, simulating the physical properties of a photovoltaic panel, built by the open- source object-oriented modeling language Modelica. The second model is data-driven, obtained by the application of machine learning techniques on the data collected in an installation of the equipment. Both approaches make use of data from the weather forecast of each day. We compare the results of the two approaches. Finally, we integrate them in more sophisticated hybrid systems that get the benefits of both.

A decision‐making experiment under wind power forecast uncertainty

AbstractAs the penetration levels of renewable energy sources increase and climatic changes produce more and more extreme weather conditions, the uncertainty of weather and power production forecasts can no longer be ignored for grid operation and electricity market bidding. In order to support the energy industry in the integration of uncertainty forecasts into their business practices, this work describes an experiment conducted with 105 participants from the energy industry. In the framework of an IEA Wind Task 36 workshop, the experiment aimed to investigate existing psychological barriers in the industry to adopt probabilistic forecasts and to better understand human decision processes. We designed and ran a ‘decision game’ to demonstrate the potential benefits of uncertainty forecasts in a realistic—although simplified—problem, where an energy trader had to decide whether to trade 100% or 50% of the energy of an offshore wind park on a given day based on deterministic and probabilistic uncertainty day‐ahead forecasts. The focus thus was on a decision‐making process dealing with extremes that can cause high costs in the form of security issues in the electric grid for system operators, or high monetary losses for traders, who have bid a power production into the market that failed to be produced due to high‐speed shutdown of the wind turbines. This paper presents the obtained results, extracts behavioural conclusions and identifies how to overcome psychological barriers to the adoption of uncertainty forecasts in the energy industry.

An Hour-Ahead PV Power Forecasting Method Based on an RNN-LSTM Model for Three Different PV Plants

Incorporating solar energy into a grid necessitates an accurate power production forecast for photovoltaic (PV) facilities. In this research, output PV power was predicted at an hour ahead on yearly basis for three different PV plants based on polycrystalline (p-si), monocrystalline (m-si), and thin-film (a-si) technologies over a four-year period. Wind speed, module temperature, ambiance, and solar irradiation were among the input characteristics taken into account. Each PV plant power output was the output parameter. A deep learning method (RNN-LSTM) was developed and evaluated against existing techniques to forecast the PV output power of the selected PV plant. The proposed technique was compared with regression (GPR, GPR (PCA)), hybrid ANFIS (grid partitioning, subtractive clustering and FCM) and machine learning (ANN, SVR, SVR (PCA)) methods. Furthermore, different LSTM structures were also investigated, with recurrent neural networks (RNN) based on 2019 data to determine the best structure. The following parameters of prediction accuracy measure were considered: RMSE, MSE, MAE, correlation (r) and determination (R2) coefficients. In comparison to all other approaches, RNN-LSTM had higher prediction accuracy on the basis of minimum (RMSE and MSE) and maximum (r and R2). The p-si, m-si and a-si PV plants showed the lowest RMSE values of 26.85 W/m2, 19.78 W/m2 and 39.2 W/m2 respectively. Moreover, the proposed method was found to be robust and flexible in forecasting the output power of the three considered different photovoltaic plants.

The Hidden-Layers Topology Analysis of Deep Learning Models in Survey for Forecasting and Generation of theWind Power and Photovoltaic Energy

As wind and photovoltaic energy become more prevalent, the optimization of power systems is becoming increasingly crucial. The current state of research in renewable generation and power forecasting technology, such as wind and photovoltaic power (PV), is described in this paper, with a focus on the ensemble sequential LSTMs approach with optimized hidden-layers topology for short-term multivariable wind power forecasting. The methods for forecasting wind power and PV production. The physical model, statistical learning method, and machine learning approaches based on historical data are all evaluated for the forecasting of wind power and PV production. Moreover, the experiments demonstrated that cloud map identification has a significant impact on PV generation. With a focus on the impact of photovoltaic and wind power generation systems on power grid operation and its causes, this paper summarizes the classification of wind power and PV generation systems, as well as the benefits and drawbacks of PV systems and wind power forecasting methods based on various typologies and analysis methods.

The smoother the better? A comparison of six post-processing methods to improve short-term offshore wind power forecasts in the Baltic Sea

Abstract. With a rapidly increasing capacity of electricity generation from wind power, the demand for accurate power production forecasts is growing. To date, most wind power installations have been onshore and thus most studies on production forecasts have focused on onshore conditions. However, as offshore wind power is becoming increasingly popular it is also important to assess forecast quality in offshore locations. In this study, forecasts from the high-resolution numerical weather prediction model AROME was used to analyze power production forecast performance for an offshore site in the Baltic Sea. To improve the AROME forecasts, six post-processing methods were investigated and their individual performance analyzed in general as well as for different wind speed ranges, boundary layer stratifications, synoptic situations and in low-level jet conditions. In general, AROME performed well in forecasting the power production, but applying smoothing or using a random forest algorithm increased forecast skill. Smoothing the forecast improved the performance at all wind speeds, all stratifications and for all synoptic weather classes, and the random forest method increased the forecast skill during low-level jets. To achieve the best performance, we recommend selecting which method to use based on the forecasted weather conditions. Combining forecasts from neighboring grid points, combining the recent forecast with the forecast from yesterday or applying linear regression to correct the forecast based on earlier performance were not fruitful methods to increase the overall forecast quality.

Short-term self consumption PV plant power production forecasts based on hybrid CNN-LSTM, ConvLSTM models

Global electricity consumption has raised in the last century due to many reasons such as the increase in human population and technological development. To keep up with this increasing trend, the use of fossil resources has increased. But these resources are not environmentally friendly, and for this reason, many countries and governments are encouraging the use of green sources. Among these sources, PV technology is widely promoted and used due to its improved efficiency and lower prices for photovoltaic panels. Therefore, the importance of forecasting power production for these plants is necessary. In this work, two hybrid models were proposed (CNN-LSTM and ConvLSTM) to effectively predict the power production of a self-consumption PV plant. To confirm the efficiency of the proposed models, the LSTM model was used as a baseline for comparison. The three models were trained on two datasets, a univariate dataset containing only the power output of the previous days, while the multivariate dataset contains more features (weather features) that affect the production of the PV plant. The time frames for the forecast ranged from one day to one week ahead of time. The results show that the proposed methods are more accurate than a normal LSTM model.

Ultra-short-term exogenous forecasting of photovoltaic power production using genetically optimized non-linear auto-regressive recurrent neural networks

Accurate and credible ultra-short-term photovoltaic (PV) power production prediction is very important in short-term resource planning, electric power dispatching, and operational security for the solar power system. This study proposes a novel approach of using genetically optimized non-linear auto-regressive recurrent neural networks (NARX) for ultra-short-term forecasting of PV power output. Hence, the high prediction accuracy of static multi-layered perceptron neural networks can be extended to dynamic (time-series) models with a more stable learning process. Exogenous models with different commonly available meteorological input parameters are developed and tested at five different locations in Algeria and Australia, as case studies of the arid desert climate. The prediction capabilities of the models are quantified as functions of the forecasting horizon (5, 15, 30, and 60 min) and the number of meteorological inputs using various statistical measures. It was found that the proposed models offer very good estimates of output power, with relative root mean square errors ranging between ∼10 and ∼20% and coefficients of determination higher than 91%, while improving the accuracy of corresponding endogenous models by up to 22.3% by only considering the day number and local time as external variables. Unlike the persistent model, the proposed NARX-GA models perform better as the forecasting horizon narrows down, with improvements of up to 58.4%.

Short-Term Forecasting of Photovoltaic Solar Power Production Using Variational Auto-Encoder Driven Deep Learning Approach

The accurate modeling and forecasting of the power output of photovoltaic (PV) systems are critical to efficiently managing their integration in smart grids, delivery, and storage. This paper intends to provide efficient short-term forecasting of solar power production using Variational AutoEncoder (VAE) model. Adopting the VAE-driven deep learning model is expected to improve forecasting accuracy because of its suitable performance in time-series modeling and flexible nonlinear approximation. Both single- and multi-step-ahead forecasts are investigated in this work. Data from two grid-connected plants (a 243 kW parking lot canopy array in the US and a 9 MW PV system in Algeria) are employed to show the investigated deep learning models’ performance. Specifically, the forecasting outputs of the proposed VAE-based forecasting method have been compared with seven deep learning methods, namely recurrent neural network, Long short-term memory (LSTM), Bidirectional LSTM, Convolutional LSTM network, Gated recurrent units, stacked autoencoder, and restricted Boltzmann machine, and two commonly used machine learning methods, namely logistic regression and support vector regression. The results of this investigation demonstrate the satisfying performance of deep learning techniques to forecast solar power and point out that the VAE consistently performed better than the other methods. Also, results confirmed the superior performance of deep learning models compared to the two considered baseline machine learning models.

A Hybrid Method for the Run-Of-The-River Hydroelectric Power Plant Energy Forecast: HYPE Hydrological Model and Neural Network

The increasing penetration of non-programmable renewable energy sources (RES) is enforcing the need for accurate power production forecasts. In the category of hydroelectric plants, Run of the River (RoR) plants belong to the class of non-programmable RES. Data-driven models are nowadays the most widely adopted methodologies in hydropower forecast. Among all, the Artificial Neural Network (ANN) proved to be highly successful in production forecast. Widely adopted and equally important for hydropower generation forecast is the HYdrological Predictions for the Environment (HYPE), a semi-distributed hydrological Rainfall–Runoff model. A novel hybrid method, providing HYPE sub-basins flow computation as input to an ANN, is here introduced and tested both with and without the adoption of a decomposition approach. In the former case, two ANNs are trained to forecast the trend and the residual of the production, respectively, to be then summed up to the previously extracted seasonality component and get the power forecast. These results have been compared to those obtained from the adoption of a ANN with rainfalls in input, again with and without decomposition approach. The methods have been assessed by forecasting the Run-of-the-River hydroelectric power plant energy for the year 2017. Besides, the forecasts of 15 power plants output have been fairly compared in order to identify the most accurate forecasting technique. The here proposed hybrid method (HYPE and ANN) has shown to be the most accurate in all the considered study cases.

A Metocean Reference Station for Offshore Wind Energy Research in the U.S.

There is an acute need for a long-term metocean reference site that will aid the near-term development of the offshore wind energy industry in the United States. High quality, multi-year observations of the wind resource at hub height in areas relevant to offshore wind energy are a requirement for reducing uncertainties in wind resource assessment, improving the fidelity of numerical models, assimilated products of the resource, and forecasts of near-term operational conditions and wind energy power production. Yet, few long-term data sets exist over the U.S. outer continental shelf that can provide accurate guidance for developers, turbine manufacturers, and utility operators. Thus, there is a critical need for robust, long-term data collection at a site having conditions representative of wind energy development areas. Additionally, an efficient and effective location for validating existing measurement systems and testing and verifying the accuracy of new sensors will play a key role in reducing uncertainty of estimates of the wind resource and reducing the cost and risk associated with offshore development.

Short-Term Forecasting of Power Production in a Large-Scale Photovoltaic Plant Based on LSTM

Photovoltaic (PV) power is attracting more and more concerns. Power output prediction, as a necessary technical requirement of PV plants, closely relates to the rationality of power grid dispatch. If the accuracy of power prediction in PV plants can be further enhanced by forecasting, stability of power grid will be improved. Therefore, a 1-h-ahead power output forecasting based on long-short-term memory (LSTM) networks is proposed. The forecasting output of the model is based on the time series of 1-h-ahead numerical weather prediction to reveal the spatio-temporal characteristic. The comprehensive meteorological conditions, including different types of season and weather conditions, were considered in the model, and parameters of LSTM models were investigated simultaneously. Analysis of prediction result reveals that the proposed model leads to a superior prediction performance compared with traditional PV output power predictions. The accuracy of output power prediction is enhanced by 3.46–13.46%.

Wind speed reconstruction using a novel Multivariate Probabilistic method and Multiple Linear Regression: advantages compared to the single correlation approach

Multivariate methods can improve wind speed prediction accuracy under specific conditions. In this work, Multiple Linear Regression (MLR) and Multiple Mass Probability (MMP) models are compared to the single correlation approach in selected tests.The novel MMP method can be trained using an unlimited number of inputs. Time series of outcome variables are then reconstructed using multiple probability functions. These functions consist of a main component, obtained from an input discrete event, and marginal components, obtained using a few nearest bins for the input variables; the use of the marginal components allows improving the algorithm's precision.A significant improvement of the wind speed prediction (by up to 52% for MMP) is achieved using four reference points. As rule of thumb, the target point is equidistant from the reference sites. In this condition, both multivariate methods perform better than the single correlation approach. This is also confirmed when estimating the Weibull parameters in a long-term period of ten years and the theoretical energy produced by two kinds of Wind Turbine Generators.Further applications of MMP concern wind power production forecast and, more in general, nonlinear relationships. In these cases, the direct use of MLR can lead to lack of accuracy.

Machine Learning-Based Prediction of Icing-Related Wind Power Production Loss

Ice-growth on wind-turbines can lead to a large reduction of energy production. Since ice-growth on the turbines is not part of standard weather prediction data, forecasts of power production can h ...

An Expectile Factor Model for Day-ahead Wind Power Forecasting

Volatile renewable power sources will soon dominate the power portfolio of leading industrial countries to match the goals of the Paris climate accord. Wind power is the leading renewable energy source. Its production is strongly volatile due to weather dependency and highly decentralised to various locations across a country. Hence, transmission and distribution system operators (TSO, DSO) and energy market participants are exposed to volume risk. Good forecasts of future wind power production are required to match residual load supply and demand in the network at any point time to circumvent load failure or frequency jumps. We propose a flexible methodology that can be scaled from a small wind farm data set to a large fleet size to forecast aggregated, turbine or farm specific wind power production. The methodology is based on multivariate expectile regressions using a factor model in combination with a vector autoregressive (VAR) time series framework with exogenous variables for forecasting purposes. Moreover, the methodology can be extended to deploy spatial information of various wind farms. We perform our analysis on a farm-level and an aggregated TSO-level data set. Results indicate out-performance of our candidate models for one day ahead forecasts. Moreover, we show that applying our candidate model affordable daily weather forecasts give better results than employing expensive intra-day weather forecasts. The model performs similarly well for farm-level data as well as for aggregated data.

Production of Hydrogen from Wind and Hydro Power in Constrained Transmission grids, Considering the Stochasticity of Wind Power

Producing hydrogen from renewable energy sources can be used as a way of extracting large quantities of energy from remote regions far from load centers. These regions have weak transmission grids and building new transmission lines are expensive due to large distances. The tight restrictions on the power systems in these regions makes daily operation difficult and unexpected variations in wind power production can have significant negative impacts, such as rationing of power.A stochastic rolling horizon model is formulated and implemented to consider the importance of including wind power stochasticity when operating flexible hydrogen loads in a congested power system. Wind power scenarios are created using realized wind power production and meteorological weather forecasts. The resulting operation plans of hydrogen storage and hydro power plants, using expected values or wind power scenarios, are tested and compared in a simulator with the realized wind power production.Results from the case study show that the stochastic model gives a better strategy than the deterministic model which use the expected value of wind production by about 5.6% and there is potential for further cost reductions by improving the forecasting. When including more than 27 wind power scenarios the changes in results are small. The case study also shows that hydrogen storage is important to avoid rationing in certain situations and increase power flow.