Photovoltaic (PV) estimation in an urban environment requires detection of rooftop area, design of PV system based on optimization on PV placement distance and the study of additional benefit of lower cooling load of building by shading provided by PV panels. The study is aimed at policymakers to introduce renewable energy policy toward net-zero energy buildings in urban areas. In this research, the capital city of Pakistan, Islamabad, is analyzed for rooftop PV capabilities using deep learning algorithms. The area of the rooftop is calculated by extracting buildings in high-resolution satellite imagery using a deep learning algorithm. The site location is analyzed for available solar energy resources. The distance between the rooftop-PV array is optimized based on self-shading losses, coefficient of performance, energy yield, net-zero energy analysis, and reduction of cooling load of the building provided by PV arrays as shading devices. The 40-km 2 area of Islamabad considered in this research can generate 1038 GWh of solar energy annually from its 4.3-km 2 rooftop area by installed capacity of 447 MW PV panels rows placed at 0.75 m apart. The electricity generated by Islamabad can curtail residential load from the national grid and form a near net-zero energy zone while the electrical energy from the grid can be provided to the industries to enhance the economy and reduce unemployment in Pakistan.

The importance of accurately forecasting the power production of wind farms is boosting the development of meteorological models and their processing. This work is a discussion of different forecast configurations for predicting the day ahead production of a wind farm sited in a moderately complex terrain. The numerical weather prediction (NWP) model MetCoOp Ensemble Prediction System with 2.5 km resolution focusing on the wind farm area is dynamically downscaled by the computational fluid model (CFD) model WindSim. The transfer of the NWP model to the CFD model can be done using NWP results from various heights above ground and using all or parts of the nodes of the NWP model within the wind farm area. In this work, many different forecasting configurations are validated and the impact on the forecast performance is discussed. The NWP-CFD downscaling results are compared to a day ahead forecast obtained through ANN methods and to the observed production. The main result of this work is that a deterministic downscaling method like CFD simulations can perform as good or better than statistical approaches when using high-resolution NWP models and more NWP model data.

The reconstruction of the angular and spatial intensity distribution from radiative flux maps measured in high flux solar simulators (HFSS) or optical concentrators is an ill-posed inverse problem requiring special solution strategies. We aimed at providing a solution strategy for the determination of intensity distributions of arbitrarily complicated concentrating facilities. The approach consists of the inverse reconstruction of the intensities from multiple radiative flux maps recorded at various positions around the focal plane. The approach was validated by three test cases including uniform spatial, Gaussian spatial, and uniform angular distributions for which we successfully predicted the intensity for a square-shaped target with edge length of 0.5 m and for a displacement range spanning ±1.5 m at a resolution of 3.2 × 10 6 elements, yielding relative errors between 19.8–26.4% and 15.7–25.6% when using Tikhonov regularization and the conjugate gradient least square (CGLS) method, respectively. The latter method showed superior performance and was used at a resolution of 2.35 × 10 7 elements to analyze EPFL's HFSS comprising 18 lamps. The inverse solution for a single lamp retrieved from experimentally measured and simulated radiative flux maps showed peak intensities of 13.7 MW/m 2 /sr and 16.0 MW/m 2 /sr, respectively, with a relative error of 81.1%. The inverse reconstruction of the entire simulator by superimposing the single lamp intensities retrieved from simulated flux maps resulted in a maximum intensity of 18.8 MW/m 2 /sr with a relative error of 80%. The inverse method proved to provide reasonable intensity predictions with limited resolution of details imposed by the high gradients in the radiative flux maps.

A direct methodology for intra-day forecasts (1–6 h ahead) of power output (PO) from photovoltaic (PV) solar plants is proposed. The forecasting methodology uses publicly available images from geosynchronous satellites to predict PO directly without resorting to intermediate irradiance (resource) forecasting. Forecasts are evaluated using four years (January 2012–December 2015) of hourly PO data from 2 nontracking, 1 MWp PV plants in California. For both sites, the proposed methodology achieves forecasting skills ranging from 24% to 69% relative to reference persistence model results, with root-mean-square error (RMSE) values ranging from 90 to 136 kW across the studied horizons. Additionally, we consider the performance of the proposed methodology when applied to imagery from the next generation of geosynchronous satellites, e.g., Himawari-8 and geostationary operational environmental satellite (GOES-R).

Day-ahead solar irradiance forecasting is carried out using data from a tropical environment, Singapore. The performance of the weather research and forecasting (WRF) model is evaluated. We explore various combinations of physics configuration setups in the WRF model and propose a setup for the tropical regions. The WRF model is benchmarked using persistence and two seasonal time series models, namely, the exponential smoothing (ETS) and seasonal autoregressive integrated moving average (SARIMA) models. It is shown that the WRF model outperforms the SARIMA model and achieves accuracies comparable with persistence and ETS models. Persistence, ETS, and WRF models have relative root mean square errors (rRMSE) of about 55–57%. Furthermore, we find that by combining the forecasting outputs of WRF and ETS models, errors can be reduced to 49%.

In this study, Reynolds-averaged Navier–Stokes (RANS) simulations are performed using the k-ε and k-ω shear stress transport (SST) turbulence closure schemes to investigate the interactions of horizontal-axis wind turbine (HAWT) models in the neutrally stratified atmospheric boundary layer (ABL). A comparative study of actuator disk, actuator line, and full rotor models of the National Renewable Energy Laboratory (NREL) 5 MW reference turbine is presented. The open-source computational fluid dynamics (CFD) code openfoam 2.1.0 and the commercial software ansysfluent 13.0 are used for simulations. Single turbine models are analyzed for turbulent structures and wake resolution in the downstream region. To investigate the influence of the incident wind field on very large turbine blades, a high-resolution full rotor simulation is carried out for a single turbine to determine blade pressure distributions. Finally, simulations are performed for two inline turbines spaced 5 diameters (5D) apart. The research presented in this study provides an intercomparison of three dominant HAWT models operating at rated conditions in a neutral ABL using an RANS framework. Furthermore, the pressure distributions of the highly resolved full rotor model (FRM) will be useful for future aeroelastic structural analysis of anisotropic composite blade materials.

The performance of buildings participating in demand response (DR) programs is usually evaluated with baseline models, which predict what electric demand would have been if a DR event had not been called. Different baseline models produce different results. Moreover, modelers implementing the same baseline model often make different model implementation choices producing different results. Using real data from a DR program in CA and a regression-based baseline model, which relates building demand to time of week, outdoor air temperature, and building operational mode, we analyze the effect of model implementation choices on DR shed estimates. Results indicate strong sensitivities to the outdoor air temperature data source and bad data filtration methods, with standard deviations of differences in shed estimates of ≈20–30 kW, and weaker sensitivities to demand/temperature data resolution, data alignment, and methods for determining when buildings are occupied, with standard deviations of differences in shed estimates of ≈2–5 kW.

In this study, a first-order environmental impact study of a large-scale deployment of solar energy-installed technologies in a complex coastal urban environment is conducted. The work is motivated by the positive prospects of building-integrated solar technologies as a sustainable alternative to energy demands and reduction of green house gases. Large-scale deployment of solar technologies in rooftops of densely populated cities may have the potential of modifying surface energy budgets resulting in cooling or heating of the urban environment. To investigate this case, a mesoscale simulation (regional atmospheric simulation system (RAMS)) effort was undertaken, with a horizontal grid resolution of 4 km on an innermost grid over Southern California (South Coast Air Basin (SoCAB)). The simulation period was selected in summer 2002 where strong urban heat islands (UHIs) were observed for the region. The urban landscape was modified to represent a percentage of the rooftops with optical and thermal properties corresponding to solar PV and thermal collectors. Results show that the large-scale presence of solar technologies in rooftops of SoCAB may have a net positive thermal storage of the buildings, an effect enhancing the existing UHI by up to 0.2 °C. This additional heat is advected inland as the sea breeze develops warming further inland areas. The net environmental effect of solar technologies when compared with solar energy production was not investigated in this study.

This paper focuses on evaluating the feasibility of estimating loads on vertical axis wind turbine blades in dynamic stall with velocity data acquired with Particle Image Velocimetry. The study uses numerical simulation data of a 2D Vertical Axis Wind Turbine in dynamic stall to verify sources of error and uncertainty and estimate the accuracy of the method. The integration of the forces from the velocity field overcomes the difficulties and limitations presented by pressure sensors for estimating the local section loads, but adds the difficulty in determining the correct velocity field and its time and spatial derivatives. The analysis also evaluates the use of phase-locked average data as an estimator of average loads.

The heliostats of central receiver solar power plants reach dimensions up to 150 m 2 with focal lengths up to 1000 m. Their optical properties and tracking accuracy have great influence on the power plant efficiency and need to be monitored both at plant start up and during operation. Up to now, there are few efficient and fast measurement techniques that allow the heliostat properties to be measured. Flux density measurements and close-range photogrammetry are possible approaches, yet they do not fulfill the requirement to be accurate, inexpensive, and fast at the same time. In this paper, we present a noncontact measurement principle, which uses edge detection to extract the heliostat and facet vertices. This information is used to calculate the surface normals. Furthermore, the corners can replace retroreflective targets generally used in close-range photogrammetry, thus, enabling a fast and completely automatic evaluation of the three-dimensional heliostat structure. The pictures are provided by a digital camera, which is mounted on a pan tilt head on top of the central receiver tower, offering visibility to all heliostats and allowing the automated qualification of whole heliostat fields in a short period of time. It is shown that measurement uncertainties in heliostat orientation for the investigated heliostat are below 4 mrad in 80% of the relevant heliostat positions. Heliostat orientation is available within three minutes. Photogrammetric measurements based on edge detection at a 40 m 2 CESA-1 heliostat at the Plataforma Solar de Almerìa exhibit an accuracy of 1.6 mrad for a single-facet normal vector with the results being available within 30 min. The reduced measurement time allows the economic characterization of entire heliostat fields. The lower accuracy compared with manual photogrammetry with retroreflective targets is still sufficient to detect facet misalignments in existing heliostat fields.

A new and fast method for optically measuring the reflector slope of parabolic troughs with high accuracy has been developed. It uses the reflection of the absorber tube in the concentrator, as seen from some distance, and is therefore called “absorber reflection method.” A digital camera is placed at a distant observation point perpendicular to the trough axis with the concentrator orientated toward it. Then, a set of pictures from the absorber tube reflection is taken with slightly different tilt angles of the concentrator. A specially developed image analysis algorithm detects the edges of the absorber tube in the reflected images. This information, along with the geometric relationship between the components, the relative collector tilt angles, and the known approximately parabolic shape of the concentrator, is used to calculate the slopes perpendicular to the trough axis. Measurement results of a EuroTrough segment of four facets are presented and verified with results from a reference measurement using high-resolution close-range photogrammetry. The results show good agreement in statistical values as well as in local values of the reflector slope. Compared to the existing photogrammetric method, the new technique reduces drastically the time measurement.

A new fast and highly accurate method for measuring the slope errors of parabolic dish concentrators has been developed. This method uses a flat target with colored stripes placed close to the focal plane of the concentrator and a digital camera located at an observation point on the optical axis at some distance from it. A specially developed image analysis algorithm detects the different colors of the images of the reflection of the target in the concentrator and assigns them their known position on the color target. This information, along with the geometry of the measurement setup and the theoretical parabolic shape of the concentrator, is used to calculate the normal vectors of the concentrator surface. From these normal vectors, the radial and tangential slopes can be calculated and compared to the design values of the concentrator. The resulting slope errors not only give the total concentrator error for general characterization of the dish but also indicate systematic errors from fabrication and installation with a high spatial resolution. In order to verify the quality of the results obtained, a ray-tracing code was developed that calculates the flux distribution on planes perpendicular to the optical axis. Measured slope errors of a 8.5 m dish concentrator are presented and the calculated flux distributions are compared to measured flux distributions. The comparison shows excellent agreement in the flux distribution on the absorber plane. This verifies the promising potential of this fast and highly precise new method for measuring imperfections in dish concentrator shape.

A method for remote optical measurement of the geometry of nonimaging concentrators is presented. A concentrator installed in a solar tower was measured by observation of transmission patterns from the heliostat field, and comparison of the measured patterns to a ray tracing simulation. The actual geometry of the concentrator was derived from optimization of the match between real and simulated patterns. The measurement was sensitive and accurate enough to detect small errors in the concentrator geometry, such as 1 millimeter in linear dimension and 0.1 ° in concentrator tilt angle. The measurement procedure is simple and can be easily adapted to a wide range of nonimaging optical systems.

An empirical or regression modeling approach is simple to develop and easy to use compared to detailed hourly simulations of energy use in commercial buildings. Therefore, regression models developed from measured energy data are becoming an increasingly popular method for determining retrofit savings or identifying operational and maintenance (O&M) problems. Because energy consumption in large commercial buildings is a complex function of climatic conditions, building characteristics, building usage, system characteristics and type of heating, ventilation, and air conditioning (HVAC) equipment used, a multiple linear regression (MLR) model provides better accuracy than a single-variable model for modeling energy consumption. Also, when hourly monitored data are available, an issue which arises is what time resolution to adopt for regression models to be most accurate. This paper addresses both these topics. This paper reviews the literature on MLR models of building energy use, describes the methodology to develop MLR models, and highlights the usefulness of MLR models as baseline models and in detecting deviations in energy consumption resulting from major operational changes. The paper first develops the functional basis of cooling energy use for two commonly used HVAC systems: dual-duct constant volume (DDCV) and dual-duct variable air volume (DDVAV). Using these functional forms, the cooling energy consumption in five large commercial buildings located in central Texas were modeled at monthly, daily, hourly, and hour-of-day (HOD) time scales. Compared to the single-variable model (two-parameter model with outdoor dry-bulb as the only variable), MLR models showed a decrease in coefficient of variation (CV) between 10 percent to 60 percent, with an average decrease of about 33 percent, thus clearly indicating the superiority of MLR models. Although the models at the monthly time scale had higher coefficient of determination (R 2 ) and lower CV than daily, hourly, and HOD models, the daily and HOD models proved more accurate at predicting cooling energy use.

A 3355 square meter, 3.3 m deep water storage pond in El Paso, Tex. was converted to a salt-gradient solar pond to supply industrial process heat to an adjacent food processing plant. Approximately 1.9 × 10 6 kg of sodium chloride salt was obtained to prepare near saturated brine for pond construction. Design and construction of the solar pond are described in detail including the lining technique, salt dissolution method, diffuser design, instrumentation, maintenance of optical clarity, and gradient establishment, including resolution of initial problems in gradient stability. The solar pond has been in continuous operation for over three years.