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This paper presents a simple forecasting database infrastructure implemented using the open-source database management system MySQL. This proposal aims at advancing the myriad of solar and wind forecast models present in the literature into a production stage. The paper gives all relevant details necessary to implement a MySQL infra-structure that collects the raw data, filters unrealistic values, classifies the data, and produces forecasts automatically and without the assistance of any other computational tools. The performance of this methodology is demonstrated by creating intra-hour power output forecasts for a 1 MW photovoltaic installation in Southern California and a 10 MW wind power plant in Central California. Several machine learning forecast models are implemented (persistence, auto-regressive and nearest neighbors) and tested. Both point forecasts and prediction intervals are generated with this methodology. Quantitative and qualitative analyses of solar and wind power forecasts were performed for an extended testing period (4 years and 6 years, respectively). Results show an acceptable and robust performance for the proposed forecasts.

Text mining is an emerging topic that advances the review of academic literature. This paper presents a preliminary study on how to review solar irradiance and photovoltaic (PV) power forecasting (both topics combined as “solar forecasting” for short) using text mining, which serves as the first part of a forthcoming series of text mining applications in solar forecasting. This study contains three main contributions: (1) establishing the technological infrastructure (authors, journals & conferences, publications, and organizations) of solar forecasting via the top 1000 papers returned by a Google Scholar search; (2) consolidating the frequently-used abbreviations in solar forecasting by mining the full texts of 249 ScienceDirect publications; and (3) identifying key innovations in recent advances in solar forecasting (e.g., shadow camera, forecast reconciliation). As most of the steps involved in the above analysis are automated via an application programming interface, the presented method can be transferred to other solar engineering topics, or any other scientific domain, by means of changing the search word. The authors acknowledge that text mining, at its present stage, serves as a complement to, but not a replacement of, conventional review papers.

This work proposes forecast models for solar-integrated, utility-scale feeders in the San Diego Gas & Electric operating region. The models predict the net load for horizons ranging from 10 to 30 min. The forecasting methods implemented include hybrid methods based on Artificial Neural Network (ANN) and Support Vector Regression (SVR), which are both coupled with image processing methods for sky images. These methods are compared against reference persistence methods. Three enhancement methods are implemented to further decrease forecasting error: (1) decomposing the time series of the net load to remove low-frequency load variation due to daily human activities; (2) segregating the model training between daytime and nighttime; and (3) incorporating sky image features as exogenous inputs in the daytime forecasts. The ANN and SVR models are trained and validated using six-month measurements of the net load and assessed using common statistic metrics: MBE, MAPE, rRMSE, and forecast skill, which is defined as the reduction of RMSE over the RMSE of reference persistence model. Results for the independent testing set show that data-driven models, with the enhancement methods, significantly outperform the reference persistence model, achieving forecasting skills (improvement over reference persistence model) as large as 43% depending on location, solar penetration and forecast horizons.

We introduce a simple and novel technique to extract dynamic features from sky images in order to increase the accuracy of intrahour forecasts for both Global Horizontal Irradiance (GHI) and Direct Normal Irradiance (DNI) values. The proposed methodology is based on a block-matching algorithm that correctly identifies the bulk motion of clouds relative to the position of the Sun in the sky. Adaptive rectangular- and wedge-shaped Regions Of Interest are used to select the image pixels for the new features. The results show an average increase of 6.8% (6.7%) in forecast skill for GHI (DNI) across all horizons tested as measured against a model with global (nonadaptive) image features. Relative to clear-sky persistence, the new model achieves skills ranging from 20% to 30% (22%–35%) for GHI (DNI), among the highest ever reported for these time horizons. An analysis based on Mutual Information and Pearson correlation coefficients between the image features and the training data reveals overall improvements in all metrics. The proposed adaptive method also improves the predictability of the ramp magnitude and direction.We introduce a simple and novel technique to extract dynamic features from sky images in order to increase the accuracy of intrahour forecasts for both Global Horizontal Irradiance (GHI) and Direct Normal Irradiance (DNI) values. The proposed methodology is based on a block-matching algorithm that correctly identifies the bulk motion of clouds relative to the position of the Sun in the sky. Adaptive rectangular- and wedge-shaped Regions Of Interest are used to select the image pixels for the new features. The results show an average increase of 6.8% (6.7%) in forecast skill for GHI (DNI) across all horizons tested as measured against a model with global (nonadaptive) image features. Relative to clear-sky persistence, the new model achieves skills ranging from 20% to 30% (22%–35%) for GHI (DNI), among the highest ever reported for these time horizons. An analysis based on Mutual Information and Pearson corre...

This work compares the performance of machine learning methods (k-nearest-neighbors (kNN) and gradient boosting (GB)) in intra-hour forecasting of global (GHI) and direct normal (DNI) irradiances. The models predict the GHI and DNI and the corresponding prediction intervals. The data used in this work include pyranometer measurements of GHI and DNI and sky images. Point forecasts are evaluated using bulk error metrics while the performance of the probabilistic forecasts are quantified using metrics such as Prediction Interval Coverage Probability (PICP), Prediction Interval Normalized Averaged Width (PINAW) and the Continuous Ranked Probability Score (CRPS). Graphical verification displays like reliability diagram and rank histogram are used to assess the probabilistic forecasts. Results show that the machine learning models achieve significant forecast improvements over the reference model. The reduction in the RMSE translates into forecasting skills ranging between 8% and 24%, and 10% and 30% for the GHI and DNI testing set, respectively. CRPS skill scores of 42% and 62% are obtained respectively for GHI and DNI probabilistic forecasts. Regarding the point forecasts, the GB method performs better than the kNN method when sky image features are included in the model. Conversely, for probabilistic forecasts the kNN exhibits rather good performance.

Short term electricity trading to balance generation and demand provides an economic opportunity to integrate larger shares of variable renewable energy sources in the power grid. Recently, many regulatory market environments are reorganized to allow short term electricity trading. This study seeks to quantify the benefits of solar forecasting for energy imbalance markets (EIM). State-of-the-art solar forecasts, covering forecast horizons ranging from 24 h to 5 min are proposed and compared against the currently used benchmark models, persistence (P) and smart persistence (SP). The implemented reforecast of numerical weather prediction time series achieves a skill of 14.5% over the smart persistence model. Using the proposed forecasts for a forecast horizon of up to 75 min for a single 1 MW power plant reduces required flexibility reserves by 21% and 16.14%, depending on the allowed trading intervals (5 and 15 min). The probability of an imbalance, caused through wrong market bids from PV solar plants, can be reduced by 19.65% and 15.12% (for 5 and 15 min trading intervals). All EIM stakeholders benefit from accurate forecasting. Previous estimates on the benefits of EIMs, based on persistence model are conservative. It is shown that the design variables regulating the market time lines, the bidding and the binding schedules, drive the benefits of forecasting.

Ground based sky imaging and irradiance sensors are used to quantitatively evaluate the impact of cloud transmittance and cloud velocity on the accuracy of short-term direct normal irradiance (DNI) forecasts. Eight representative partly-cloudy days are used as an evaluation dataset. Results show that incorporating real-time sky and cloud transmittances as inputs reduces the root mean square error (RMSE) of forecasts of both the Deterministic model (Det) (16.3%∼ 17.8% reduction) and the multi-layer perceptron network model (MLP) (0.8% ∼ 6.2% reduction). Four computer vision methods: the particle image velocimetry method, the optical flow method, the x-correlation method and the scale-invariant feature transform method have accuracies of 83.9%, 83.5%, 79.2% and 60.9% in deriving cloud velocity, with respect to manual detection. Analysis also shows that the cloud velocity has significant impact on the accuracy of DNI forecasts: underestimating the cloud velocity magnitude by 50% results in 30.2% (Det) and 24.2% (MLP) increase of forecast RMSE; a 50% overestimate results in 7.0% (Det) and 8.4% (MLP) increase of RMSE; a ±30∘ deviation of cloud velocity direction increases the forecast RMSE by 6.2% (Det) and 6.6% (MLP).

The purpose of this work is to present a simple global solar irradiance forecasting framework based on the optimization of the k -nearest-neighbors (kNN) and artificial neural networks algorithms (ANN) for time horizons ranging from 15 min to 2 h. We apply the proposed forecasting models to irradiance from five locations and assessed the impact of different micro-climates on forecasting performance. We also propose two metrics, the density of large irradiance ramps and the time series determinism, to characterize the irradiance forecastability. Both measures are computed from the irradiance time series and provide a good indication for the forecasting performance before any predictions are produced. Results show that the proposed kNN and ANN models achieve substantial improvements relative to simpler forecasting models. The results also show that the optimal parameters for the kNN and ANN models are highly dependent on the different micro-climates. Finally, we show that the density of large irradiance ramps and time series determinism can successfully explain the forecasting performance for the different locations and time horizons.

We develop a hybrid, real-time solar forecasting computational model to construct prediction intervals (PIs) of one-minute averaged direct normal irradiance for four intra-hour forecasting horizons: five, ten, fifteen, and 20 min. This hybrid model, which integrates sky imaging techniques, support vector machine and artificial neural network sub-models, is developed using one year of co-located, high-quality irradiance and sky image recording in Folsom, California. We validate the proposed model using six-month of measured irradiance and sky image data, and apply it to construct operational PI forecasts in real-time at the same observatory. In the real-time scenario, the hybrid model significantly outperforms the reference persistence model and provides high performance PIs regardless of forecast horizon and weather condition.

This work compares various CES (cryogenic energy storage) systems as possible candidates to store energy from renewable sources. Mitigating solar and wind power variability and its direct effect on local grid stability are already a substantial technological bottleneck for increasing market penetration of these technologies. In this context, CES systems represent low-cost solutions for variability that can be used to set critical power ramp rates. We investigate the different thermodynamic and engineering constraints that affect the design of CES systems, presenting theoretical simulations, indicating that optimization is also needed to improve the cryogenic plant performance.

This work proposes a novel forecast methodology for intra-hour solar irradiance based on optimized pattern recognition from local telemetry and sky imaging. The model, based on the k -nearest-neighbors ( k NN) algorithm, predicts the global (GHI) and direct (DNI) components of irradiance for horizons ranging from 5 min up to 30 min, and the corresponding uncertainty prediction intervals. An optimization algorithm determines the best set of patterns and other free parameters in the model, such as the number of nearest neighbors. Results show that the model achieves significant forecast improvements (between 10% and 25%) over a reference persistence forecast. The results show that large ramps in the irradiance time series are not very well capture by the point forecasts, mostly because those events are underrepresented in the historical dataset. The inclusion of sky images in the pattern recognition results in a small improvement (below 5%) relative to the k NN without images, but it helps in the definition of the uncertainty intervals (specially in the case of DNI). The prediction intervals determined with this method show good performance, with high probability coverage (≈90% for GHI and ≈85% for DNI) and narrow average normalized width (≈8% for GHI and ≈17% for DNI).

Refrigerant blends and pure refrigerants have wide applicability in thermal engineering. One of the cri- tical parameters in the design and evaluation of thermal equipment is the heat transfer coefficient, which can be difficult to determine for refrigerants that undergo phase change within the equipment. For pure refrigerants, classical experimental relations developed by Gungor and Winterton (GW87) are known to exhibit errors around 15% on average, and reaching more than 40% in some cases. For refrigerant blends larger uncertainties are expected due to a complex number of factors such as nucleate boiling degrada- tion, particularly when using functional forms previously developed for pure refrigerants. This work provides a comprehensive experimental study on the determination of heat transfer coefficients for R-22, R-134a, and the predefined refrigerant blends R-404A and R-407C. Genetic optimization is used to obtain more accurate semi empirical relations based on the classical GW87 correlation, and results of the optimization analysis show large improvement for pure refrigerants. The use of a degradation fac- tor in the optimized correlation for R-407C allows for substantial error reduction for refrigerant blends.

We propose and analyze a spatioetemporal correlation method to improve forecast performance of solar irradiance using gridded satellite-derived global horizontal irradiance (GHI) data. Forecast models are developed for seven locations in California to predict 1-h averaged GHI 1, 2 and 3 h ahead of time. The seven locations were chosen to represent a diverse set of maritime, mediterranean, arid and semi-arid micro-climates. Ground stations from the California Irrigation Management Information System were used to obtain solar irradiance time-series from the points of interest. In this method, firstly, we define areas with the highest correlated time-series between the satellite-derived data and the ground data. Secondly, we select satellite-derived data from these regions as exogenous variables to several forecast models (linear models, Artificial Neural Networks, Support Vector Regression) to predict GHI at the seven locations. The results show that using linear forecasting models and a genetic algorithm to optimize the selection of multiple time-lagged exogenous variables results in significant forecasting improvements over other benchmark models.

We develop a standalone, real-time solar forecasting computational platform to predict one minute averaged solar irradiance ramps ten minutes in advance. This platform integrates cloud tracking techniques using a low-cost fisheye network camera and artificial neural network (ANN) algorithms, where the former is used to introduce exogenous inputs and the latter is used to predict solar irradiance ramps. We train and validate the forecasting methodology with measured irradiance and sky imaging data collected for a six-month period, and apply it operationally to forecast both global horizontal irradiance and direct normal irradiance at two separate locations characterized by different micro-climates (coastal and continental) in California. The performance of the operational forecasts is assessed in terms of common statistical metrics, and also in terms of three proposed ramp metrics, used to assess the quality of ramp predictions. Results show that the forecasting platform proposed in this work outperforms the reference persistence model for both locations.

A smart, real-time reforecast method is applied to the intra-hour prediction of power generated by a 48 MWe photovoltaic (PV) plant. This reforecasting method is developed based on artificial neural network (ANN) optimization schemes and is employed to improve the performance of three baseline prediction models: (1) a physical deterministic model based on cloud tracking techniques; (2) an auto-regressive moving average (ARMA) model; and (3) a k-th Nearest Neighbor (kNN) model. Using the measured power data from the PV plant, the performance of all forecasts is assessed in terms of common error statistics (mean bias, mean absolute error and root mean square error) and forecast skill over the reference persistence model. With the reforecasting method, the forecast skills of the three baseline models are significantly increased for time horizons of 5, 10, and 15 min. This study demonstrates the effectiveness of the optimized reforecasting method in reducing learnable errors produced by a diverse set of forecast methodologies.

International audience; In this work, we assess the performance of three probabilistic models for intra-day solar forecasting. More precisely, a linear quantile regression method is used to build three models for generating 1 h–6 h-ahead probabilistic forecasts. Our approach is applied to forecasting solar irradiance at a site experiencing highly variable sky conditions using the historical ground observations of solar irradiance as endogenous inputs and day-ahead forecasts as exogenous inputs. Day-ahead irradiance forecasts are obtained from the Integrated Forecast System (IFS), a Numerical Weather Prediction (NWP) model maintained by the European Center for Medium-Range Weather Forecast (ECMWF). Several metrics, mainly originated from the weather forecasting community, are used to evaluate the performance of the probabilistic forecasts. The results demonstrated that the NWP exogenous inputs improve the quality of the intra-day probabilistic forecasts. The analysis considered two locations with very dissimilar solar variability. Comparison between the two locations highlighted that the statistical performance of the probabilistic models depends on the local sky conditions.

We propose a novel methodology to select candidate locations for solar power plants that take into account solar variability and geographical smoothing effects. This methodology includes the development of maps created by a clustering technique that determines regions of coherent solar quality attributes as defined by a feature which considers both solar clearness and solar variability. An efficient combination of two well-known clustering algorithms, the affinity propagation and the k -means, is introduced in order to produce stable partitions of the data to a variety of number of clusters in a computationally fast and reliable manner. We use 15 years worth of the 30-min GHI gridded data for the island of Lanai in Hawaii to produce, validate and reproduce clustering maps. A family of appropriate number of clusters is obtained by evaluating the performance of three internal validity indices. We apply a correlation analysis to the family of solutions to determine the map segmentation that maximizes a definite interpretation of the distinction between and within the emerged clusters. Having selected a single clustering we validated the clustering by using a new dataset to demonstrate that the degree of similarity between the two partitions remains high at 90.91%. In the end we show how the clustering map can be used in solar energy problems. Firstly, we explore the effects of geographical smoothing in terms of the clustering maps, by determining the average ramp ratio for two location within and without the same cluster and identify the pair of clusters that shows the highest smoothing potential. Secondly, we demonstrate how the map can be used to select locations for GHI measurements to improve solar forecasting for a PV plant, by showing that additional measurements from within the cluster where the PV plant is located can lead to improvements of 10% in the forecast.

This study proposes an automatic smart adaptive cloud identification (SACI) system for sky imagery and solar irradiance forecast. The system is deployed using off-the-shelf fish-eye cameras that offer substantial advantages in terms of cost when compared to industry-standard sky imagers. SACI uses a smart image categorization (SIC) algorithm that combines the sky images and solar irradiance measurements to classify sky conditions into three categories: clear, overcast, and partly cloudy. A cloud detection scheme, optimized for each image category, is used to quantify cloud cover from the sky images. SACI is optimized and validated against manually annotated images. Results show that SACI achieves overall classification accuracy higher than 90% and outperforms reference cloud detection methods. Cloud cover retrieved by SACI is used as an input for an artificial neural network (ANN) model that predicts 1-min average global horizontal irradiance (GHI), 5-, 10-, and 15-min ahead of time. The performance of the ANN forecasting model is assessed in terms of common error statistics (mean bias and root-mean-square error) and in terms of forecasting skill over persistence. The model proposed in this work achieves forecasting skills above 14%, 18%, and 19% over the persistence forecast for 5-, 10-, and 15-min forecasts, respectively.

We present a novel method for cloud tracking based on total sky imaging to forecast intra-hour Direct Normal Irradiance (DNI). We introduce both a sector method used to detect the direction of motion of potentially sun-blocking clouds, and a adjustable-ladder method, which is based on a size-adjustable set of grid elements that focus on sky regions of greatest potential for affecting ground DNI values. Images taken every 20 s by a sky imager are processed to generate 1-min based DNI forecasts for up to 20 min ahead horizons. Our results show that the deterministic DNI forecasting using sector-ladder methods performs better than the Particle Image Velocimetry (PIV) methods, showing improved performance in both detection of cloud motion and DNI forecast under different sky conditions: broken-sky, clear-sky and overcast.

Electric load forecasting is a key element for management and operation of the electric grid. In this study we introduce ensemble re-forecast methods that take an initial forecast and produce a better prediction by extracting information from the structured errors. The models in the ensemble rely upon the real-time information obtained from load measurements and estimates over a state-wide domain. The weights in the ensemble are optimized in three different ways based on global, hourly, and weekly performance of the models. The proposed methodology is applied to predict hour-ahead market (HAM) and day-ahead market (DAM) load for California Independent System Operator (CAISO) and Electric Reliability Council of Texas (ERCOT) respectively. Proposed models showed consistent performance enhancements for all the cases. HAM predictions show an improvement of 47% and 36% in terms of Mean Absolute Percentage Error over the forecasts provided by CAISO and ERCOT. For DAM, the improvements are 34% for CAISO and 47% for ERCOT. Temporal analysis comparing the internal forecast produced by the ISOs and re-forecasts shows significant improvement during off-peak hours and small improvement for on-peak hours. Results validate the potential of the proposed methodology to enhance the forecast accuracy, independent of load profile or forecast horizon.

Two-phase heat transfer coefficients for internal flows play a critical role in the design and analysis of evaporators and condensers. Previous studies propose empirical relations that combine the effects of nucleate and convective boiling onto the overall heat transfer coefficient. Although these relatively simple empirical relations offer physical insight on the nucleation, boiling and flow processes, they come at the expense of some computational accuracy. In this work, we explored new techniques to determine two-phase heat transfer coefficients for refrigerants R-22, R-134a and R-404a. We used multiple functional forms for the heat transfer coefficients and considered multiple dimensionless parameters as inputs to the algebraic relations. We used genetic algorithms to search the solution space that consists of the input parameters plus the different functional forms, and obtained optimal empirical correlations that cover a wide range of heat transfer regimes. Then, we combined genetic algorithm and artificial neural networks to obtain a more universal correlation. Two versions were developed for each correlation: one that assumes a priori knowledge of the local heat flux and another that does not. Several error metrics were computed for all the correlations developed and compared against correlations from the literature. We conclude that substantial improvements can be achieved in both accuracy and robustness of the correlations by using advanced optimization techniques.

We describe and release a comprehensive solar irradiance, imaging, and forecasting dataset. Our goal with this release is to provide standardized solar and meteorological datasets to the research community for the accelerated development and benchmarking of forecasting methods. The data consist of three years (2014–2016) of quality-controlled, 1-min resolution global horizontal irradiance and direct normal irradiance ground measurements in California. In addition, we provide overlapping data from commonly used exogenous variables, including sky images, satellite imagery, and Numerical Weather Prediction forecasts. We also include sample codes of baseline models for benchmarking of more elaborated models.We describe and release a comprehensive solar irradiance, imaging, and forecasting dataset. Our goal with this release is to provide standardized solar and meteorological datasets to the research community for the accelerated development and benchmarking of forecasting methods. The data consist of three years (2014–2016) of quality-controlled, 1-min resolution global horizontal irradiance and direct normal irradiance ground measurements in California. In addition, we provide overlapping data from commonly used exogenous variables, including sky images, satellite imagery, and Numerical Weather Prediction forecasts. We also include sample codes of baseline models for benchmarking of more elaborated models.

We realized long ago that across the natural world it is possible to find structures, materials, and processes that are of great interest for human applications. In fact, attempts to copy nature to obtain new technologies or improve existing ones go back thousands of years, from the Chinese trying to make artificial silk to Leonardo da Vinci?s designs of flying machines inspired by the flight of birds. As our ability to explore nature increased, so has the perception that we are surrounded by natural designs that surpass most of our own and that great benefits can be achieved by mimicking nature.

We propose novel smart forecasting models for Direct Normal Irradiance (DNI) that combine sky image processing with Artificial Neural Network (ANN) optimization schemes. The forecasting models, which were developed for over 6 months of intra-minute imaging and irradiance measurements, are used to predict 1 min average DNI for specific time horizons of 5 and 10 min. We discuss optimal models for low and high DNI variability seasons. The different methods used to develop these season-specific models consist of sky image processing, deterministic and ANN forecasting models, a genetic algorithm (GA) overseeing model optimization and two alternative methods for training and validation. The validation process is carried over by the Cross Validation Method (CVM) and by a randomized training and validation set method (RTM). The forecast performance for each solar variability season is evaluated, and the models with the best forecasting skill for each season are selected to build a hybrid model that exhibits optimal performance for all seasons. An independent testing set is used to assess the performance of all forecasting models. Performance is assessed in terms of common error statistics (mean bias and root mean square error), but also in terms of forecasting skill over persistence. The hybrid forecast models proposed in this work achieve statistically robust forecasting skills in excess of 20% over persistence for both 5 and 10 min ahead forecasts, respectively.

The higher penetration of renewable resources in the energy portfolios of several communities accentuates the need for accurate forecasting of variable resources (solar, wind, tidal) at several different temporal scales in order to achieve power grid balance. Solar generation technologies have experienced strong energy market growth in the past few years, with corresponding increase in local grid penetration rates. As is the case with wind, the solar resource at the ground level is highly variable mostly due to cloud cover variability, atmospheric aerosol levels, and indirectly and to a lesser extent, participating gases in the atmosphere. The inherent variability of solar generation at higher grid penetration levels poses problems associated with the cost of reserves, dispatchable and ancillary generation, and grid reliability in general. As a result, high accuracy forecast systems are required for multiple time horizons that are associated with regulation, dispatching, scheduling and unit commitment. Here we review the theory behind these forecasting methodologies, and a number of successful applications of solar forecasting methods for both the solar resource and the power output of solar plants at the utility scale level.

Net energy metering tariffs have encouraged the growth of solar PV in the distribution grid. The additional variability associated with weather-dependent renewable energy creates new challenges for power system operators that must maintain and operate ancillary services to balance the grid. To deal with these issues power operators mostly rely on demand load forecasts. Electric load forecast has been used in power industry for a long time and there are several well established load forecasting models. But the performance of these models for future scenario of high renewable energy penetration is unclear. In this work, the impact of onsite solar power generation on the demand load forecast is analyzed for a community that meets between 10% and 15% of its annual power demand and 3–54% of its daily power demand from a solar power plant. Short-Term Load Forecasts (STLF) using persistence, machine learning and regression-based forecasting models are presented for two cases: (1) high solar penetration and (2) no penetration. Results show that for 1-h and 15-min forecasts the accuracy of the models drops by 9% and 3% with high solar penetration. Statistical analysis of the forecast errors demonstrate that the error distribution is best characterized as a t -distribution for the high penetration scenario. Analysis of the error distribution as a function of daily solar penetration for different levels of variability revealed that the solar power variability drives the forecast error magnitude whereas increasing penetration level has a much smaller contribution. This work concludes that the demand forecast error distribution for a community with an onsite solar generation can be directly characterized based on the local solar irradiance variability.

This work describes a new hybrid method that combines information from processed satellite images with Artificial Neural Networks (ANNs) for predicting global horizontal irradiance (GHI) at temporal horizons of 30, 60, 90, and 120 min. The forecast model is applied to GHI data gathered from two distinct locations (Davis and Merced) that represent well the geographical distribution of solar irradiance in the San Joaquin Valley. The forecasting approach uses information gathered from satellite image analysis including velocimetry and cloud indexing as inputs to the ANN models. To the knowledge of the authors, this is the first attempt to hybridize stochastic learning and image processing approaches for solar irradiance forecasting. We compare the hybrid approaches using standard error metrics to quantify the forecasting skill for the several time horizons considered. 2013 Elsevier Ltd. All rights reserved.

The higher penetration of renewable resources in the energy portfolios accentuates the need for accurate forecasting of variable resources (solar, wind, tidal) at several different temporal scales to achieve power grid balance. Solar generation technologies have experienced strong energy market growth in the past few years, with corresponding increase in local grid penetration rates. As is the case with wind, the solar resource at the ground level is highly variable mostly due to cloud cover variability, atmospheric aerosol levels, and indirectly, and to a lesser extent, participating gases in the atmosphere. The inherent variability of solar generation at higher grid penetration levels poses problems associated with the cost of reserves, dispatchable and ancillary generation, and grid reliability in general. As a result, high accuracy forecast systems are required for multiple time horizons that are associated with regulation, dispatching, scheduling, and unit commitment. Here we review the theory behind mathematical methods for optimized solar forecasting and a number of successful applications of solar forecasting methods for both the solar resource and the power output of solar plants at the utility scale level.

This work advances our understanding of the drag force acting on a particle due to the oscillatory flow of a viscous fluid with finite Reynolds and Strouhal numbers. The drag force is is determined using the novel concept of variable order (VO) calculus, where the order of derivative can vary with the parameters and variables, according to the dynamics of the flow. Using the VO formulation we determine: (i) The region of validity of Tchen's equation for oscillatory flow, (ii) the region where the order of the derivative is fractional but constant, and (iii) the region where the strong non-linearity of the flow requires a variable order derivative to account for the increased complexity of the flow.

In this chapter, we discuss nonlinear regression and stochastic-learning methods for solar forecasting. A detailed comparison of nonstationary regression methods and different stochastic-learning methods based on kNN, ANN, and GA is presented. A hybrid GA/ANN method emerges as the most robust stochastic candidate to be used as the basis for development of high-fidelity forecast engines. We illustrate different applications of stochastic-learning by considering univariate and multivariate inputs, and we highlight some of the robust qualities of stochastic-learning for a wide range of time horizons.

The present work concerns the formal evolutionary development of complex structural engineering systems using a biologically inspired evolutionary method. The bioinspired method presented in this study uses Map Lindenmayer systems, or more specifically mBPMOL-systems, which create single layered cellular structures, to mimic the biological process of cellular division. The problem here reported is a very simple one, which allows for the proof of concept in a very clear manner. It consists in finding the optimal cellular structure for a plate loaded transversely. The structures created with the mBPMOL-systems are then analyzed using the finite element method (FEM) to determine its structural performance. An evolutionary algorithm, such as the genetic algorithm is used to evolve the topology of the cellular structures.

this range of Re numbers is necessary. Recently was reported 2 that the humpback whale flipper is optimized to prevent stall and to improve aerodynamic performance. These features allow these animals to be extremely mobile with great turning ability which is necessary to catch prey. This observation together with the fact that the Reynolds number for the humpback whale falls in the aforementioned low Re range propelled the experimental study the humpback whale flipper. The flippers for this species display a very characteristic scalloped leading edge, whereas the flippers of other species less maneuverable are much smoother. 3 The experiments compared a flipper with tubercles with a smooth flipper. The researchers reported an improvement in the aerodynamic performance as well an increase in the angle of attack at which the flipper stalls. However no flow visualization was performed, therefore the reasons why the scalloped flipper performs better were not uncovered. In this work we performed the numerical simulation of the setup used for the experimental study. The unsteady turbulent flow field for the scalloped flipper and for the smooth flipper was accurately determined which produced detailed information necessary to fully understand the mechanism behind the reported improvement. Our goal with this work is to increase the knowledge about these lower Re number flows which will be useful for the design of more ecient UAV’s wings.

This work concerns the numerical investigation of the impact of a wave on a square column. The wave is generated by a dam break in a wave tank. Two turbulence models were used: Large Eddy Simulations (LES) and Unsteady Reynolds Averaged Navier-Stokes (URANS). The numerical simulations were carried out using a finite volume approximation and the SIMPLE algorithm for the solution of the governing equations. Turbulence was modeled with the standard Smagorinsky-Lilly subgrid-model for the LES and the standard κ-e model for the URANS. The results are validated against experimental data for the wave impact on a square column facing the flow. The results, especially for LES, show very good agreement between the predictions and experimental results. The overall accuracy of the LES, as expected, is superior to the URANS. However, if computational resources are limited, URANS can still provide satisfactory results for structural design.Copyright © 2007 by ASME

The determination of a consistent equation of motion for spherical particles in finite and infinitesimal Reynolds numbers is a classical problem in fluid mechanics. A number of keystone steps to develop and manipulate such Lagrangian equations of motion have been discussed in a steady stream of papers, starting at the end of the 19th century. The problem is of great complexity even for the limit of infinitesimal Reynolds numbers when both low and high frequency flows are considered. The complexity of the problem is evident when one considers that the surrounding fluid is continuously being deformed by the presence of the particle and therefore is continuously responding to particle accelerations according to its own inertialviscous balance. This intricate interaction translates into a striking feature: the so called history or Basset force. This force, which accounts for the effects of the local acceleration of the flow, is represented by an integro-differential operator. Coimbra and Rangel were the first to realize that the Basset force can ∗Graduate student, Department of Mechanical Engineering, 2540 Dole Street – Holmes Hall 302. †Assistant Professor, Department of Mechanical Engineering/SMA, Av. Rovisco Pais,1. ‡Associate Professor, Department of Mechanical Engineering, 2540 Dole Street – Holmes Hall 302. §Assistant Professor, Department of Mechanical Engineering, 2540 Dole Street – Holmes Hall 302. Copyright c © 2005 by H. T. C. Pedro, J. M. C. Pereira, M. H. Kobayashi and C. F. M. Coimbra. Published by the American Institute of Aeronautics and Astronautics, Inc. with permission.