Operating re-configurable solar energy generators for increasing yield during non-ideal weather conditions

Methods, systems, and computer program products for operating re-configurable solar energy generators for increasing yield during non-ideal weather conditions are provided herein. A computer-implemented method includes determining, for each of multiple portions of the sky, by using one or more machine learning algorithms, a respective level of diffuse irradiance corresponding to image data from that portion; identifying one or more portions of the image data corresponding to the multiple portions of sky image data that include a higher level of diffuse irradiance, as compared to other portions of the image data; and configuring one or more solar photovoltaic modules based at least in part on the one or more identified portions of image data that include a higher level of diffuse irradiance.

FIELD

The present application generally relates to information technology, and, more particularly, to renewable energy generation and management.

BACKGROUND

Large solar energy production projects commonly suffer from uncertain yields that can be related to variable weather conditions as well as inefficient grid planning and distribution. In an attempt to increase power production, various configurable add-ons such as reflectors, mirrors etc., are occasionally used in existing approaches. Such configurable add-ons generally require predictions and/or short-term forecasts of solar irradiance, which can be challenging, particularly during cloudy weather conditions. While clear sky models (geometry-based) are applicable on clear days, the azimuth of the sun is typically not indicative of the diffuse irradiance. Diffuse irradiance (DI) can include reflected or diffused light passing through clouds and/or off nearby objects, ground surfaces, etc. DI can also change rapidly with cloud movement, and DI is location- and environment-specific, not correlated with latitude.

SUMMARY

In one embodiment of the present invention, techniques for operating re-configurable solar energy generators for increasing yield during non-ideal weather conditions are provided. An exemplary computer-implemented method can include determining, for each of multiple portions of the sky, by using one or more machine learning algorithms, a respective level of diffuse irradiance corresponding to image data from that portion, wherein the one or more machine learning algorithms comprise at least one regression function that analyzes irradiance vectors from image data corresponding to the multiple portions of the sky, and wherein the determining comprises, for the image data corresponding to each of the multiple portions of the sky, decoupling an irradiance vector into (i) a direct irradiance component and (ii) a diffuse irradiance component. Such a method can also include identifying one or more portions of the image data corresponding to the multiple portions of sky image data that include a higher level of diffuse irradiance, as compared to other portions of the image data, and configuring one or more solar photovoltaic modules based at least in part on the one or more identified portions of image data that include a higher level of diffuse irradiance.

In another embodiment of the invention, an exemplary computer-implemented method can include generating a machine learning model for determining irradiance levels, wherein the machine learning model is based at least in part on (i) sky image data captured over a temporal period and (ii) one or more weather conditions, corresponding to the sky image data, sensed over the temporal period. The method can also include capturing, using multiple cameras, image data specific to one or more regions of sky, sensing one or more transient weather conditions at geographic regions corresponding to the one or more regions of sky, and applying the machine learning model to (i) the captured image data specific to the one or more regions of sky and (ii) the one or more transient weather conditions sensed at the geographic regions corresponding to the one or more regions of sky. Further, the method can include outputting, based on implementation of the machine learning model, (i) sector-by-sector levels of produced diffuse irradiance and (ii) sector-by-sector levels of produced direct irradiance; and configuring one or more solar photovoltaic modules based at least in part on (i) the sector-by-sector levels of produced diffuse irradiance and (ii) the sector-by-sector levels of produced direct irradiance.

Another embodiment of the invention or elements thereof can be implemented in the form of a computer program product tangibly embodying computer readable instructions which, when implemented, cause a computer to carry out a plurality of method steps, as described herein. Furthermore, another embodiment of the invention or elements thereof can be implemented in the form of a system including a memory and at least one processor that is coupled to the memory and configured to perform noted method steps. Yet further, another embodiment of the invention or elements thereof can be implemented in the form of means for carrying out the method steps described herein, or elements thereof; the means can include hardware module(s) or a combination of hardware and software modules, wherein the software modules are stored in a tangible computer-readable storage medium (or multiple such media).

DETAILED DESCRIPTION

As described herein, an embodiment of the present invention includes operating re-configurable solar energy generators for increasing yield during non-ideal weather conditions. At least one embodiment of the invention includes operating and/or adjusting solar energy generators with one or more re-configurable add-ons, by deploying one or more sky cameras to guide a control system using processed image data obtained from the sky cameras. Such an embodiment includes calculating the (circular) area of sky covered by each sky camera, dividing the (circular) area into different equidistant sectors, and predicting diffuse irradiance via a machine learning regression model, wherein a given sector of an image regresses towards the diffuse solar irradiance. Accordingly, such an embodiment includes localization of diffuse irradiance sources in (portions of) the sky.

Additionally, one or more embodiments of the invention include calculating the diffuse and direct irradiance obtained for each sector using a machine learning algorithm, and adjusting one or more photovoltaic modules (such as solar panels) and/or configurable add-ons to the sectors producing a higher amount of diffuse solar irradiance based on the output obtained from the machine learning algorithm.

As further detailed herein, at least one embodiment of the invention includes implementation of one or more sky cameras deployed in connection with a solar farm that includes one or more re-configurable add-ons. Additionally, the solar panels and/or add-ons of such a solar farm can be automatically or manually controlled and/or guided based at least in part on processed image data obtained by the one or more sky cameras. Such an embodiment can include improving yields of a photonic harvesting project carried out in connection with the solar farm.

As detailed herein, solar irradiance components include diffuse horizontal irradiance (DHI), direct normal irradiance (DNI), and global horizontal irradiance (GHI). DNI refers to the amount of solar radiation received per unit area by a surface that is perpendicular to incoming solar radiation. DHI refers to the amount of radiation received per unit area by a surface, wherein the radiation has been scattered by particles in the atmosphere and can reach the surface from multiple directions. GHI refers to the total amount of solar radiation received by a surface horizontal to the ground. Additionally, GHI includes both DNI and DHI. In one or more embodiments of the invention, DHI is a significant fraction of both GHI and DNI.

At least one embodiment of the invention includes calculating the optimal angle to maximize yield in a photonic harvesting setup with a flat solar panel and a single plane mirror (reflector) with an adjustable angle Θ. For a given configuration angle Θ, the set of available (non-occluded) “T” arcs are pre-computed with geometric view-point analysis during a system calibration phase. A machine learning model can be implemented as a regression function (Fsky) that is pre-trained to regress to solar irradiance components with a sector-wise spatial spread. The training data can include input sky image data (which can, for example, be preprocessed to enhance presence of light sources), sector-arc boundaries, total direct and diffuse irradiance measured at one or more points, and/or one or more weather measurements (temperature, wind speed, barometric pressure, etc.). The regression computed using a learning algorithm is then further constrained, such that the total measured irradiance is equally partitioned into the sectors. The training process can be improved, for example, with fine-grain training data that can include exclusively measured diffuse irradiance, etc.

In at least one embodiment of the invention, multiple sensors (or a network of sky cameras) are placed at appropriate distances to measure irradiance specific to multiple local sectors. A learning model can accept, as input, the individual sectors of an image and one or more local weather parameters. This model learns a representation of one or more portions of sky from image characteristics and external weather data. The output for the model can include produced values of sector-by-sector diffuse and direct solar irradiance. Additionally, using a network of pyranometers, one or more embodiments of the invention can include calculating each sector's individual diffuse and direct solar irradiance. This calculated value can then be used as the ground truth to learn the model. The model, which can include a machine learning model, regresses towards the correct diffuse solar irradiance.

Also, in one or more embodiments of the invention, ground surface diffuse irradiance can be measured using a masking approach to record the halo effect on the corners of each sky camera lens. The light sensed from the sky camera lens edges can be measured by comparing that sensed light with the expected irradiance determined from nearby regions. During a training phase, such a measurement can be validated with deployed sensors and/or other sky cameras.

FIG. 1is a diagram illustrating system architecture, according to an embodiment of the invention. By way of illustration,FIG. 1depicts a solar farm102, which includes photovoltaic modules, add-on features, as well as one or more sky cameras104. Additionally, as illustrated,FIG. 1depicts a prediction module106, which uses input from the sky cameras104to generate an output that is provided to a control system108(which is linked to the photovoltaic modules and add-on features of the solar farm102).

More specifically, using the sky cameras104(that is, upward facing cameras with wide angled lenses that capture images of sky), one or more embodiments of the invention can include detecting diffuse irradiance sources in one or more portions of the sky. As further detailed herein, sky cameras can include cameras refactored for a set of special applications related to solar irradiance and cloud cover estimation and forecasting. In one or more embodiments of the invention, certain types of diffused light can be better captured with a halo disk suspended over a sky camera. As depicted inFIG. 1, sky cameras104can capture photographic images of the sky that are used as input by a prediction module106.

In addition to now-casting (based on the input from the sky cameras104), the prediction module106can perform future and/or near-time forecasting with respect to irradiance. For example, the prediction module106can compute direct and diffuse solar irradiance forecasting based at least in part on the sky camera images, and can subsequently provide such computations to the control system108, which can, based on the provided forecast computations, configure the photovoltaic modules and/or add-on features of the solar farm102to generate higher yield(s).

Accordingly, and as further detailed herein, one or more embodiments of the invention include configuring one or more solar panels and/or one or more reflectors to increase solar energy yield. Such an embodiment includes using sky camera image data to predict which region(s) of a celestial dome is/are the brightest. The sky cameras can be implemented to monitor and/or capture data pertaining to clear sky regions and well as regions with cloud cover occluding the sun. At least one embodiment of the invention can additionally include determining levels of diffuse irradiance obtained and/or derived from terrestrial or ground sources and elements such as mountains, snow, salt, sand, high-rises, and/or other obstructions on the ground.

FIG. 2is a diagram illustrating irradiance angles at different times of day, according to an exemplary embodiment of the invention. By way of illustration,FIG. 2depicts a photovoltaic module202and a mirror204. In the example depicted inFIG. 2, the angle (θ) formed by the photovoltaic module202and the mirror204is 60 degrees. Accordingly, a reflection analysis (sun or cloud) of such a configuration includes 60 degrees of harvestable incident angles, while light from other directions can be occluded. If the amount of light harvested from angle portions A and B (corresponding to 11:00 AM and 12:00 PM, respectively) is greater than the amount of light lost from angle portions E and F (corresponding to 2:00 PM and 3:00 PM, respectively), a gain on DNI is expected.

FIG. 3is a diagram illustrating sky images with irradiance predictions, according to an exemplary embodiment of the invention. By way of illustration,FIG. 3depicts a sector-based approach of sky analysis, wherein element302depicts an original frame and element304depicts a processed frame with sector-by-sector irradiance prediction. Input data, from one or more sky cameras, used to generate a processed frame such as element304can include visible two-dimensional (2D) information, visible and near-infrared (NIR) 2D information, and/or three-dimensional (3D) projected images of the celestial dome. Additionally, in one or more embodiments of the invention (such as depicted inFIG. 3), the hemispherical region absorbed by the one or more sky cameras is partitioned into non-overlapping sections that are split by cross-sectional cuts with concentric circles.

By way of further example and/or illustration, at least one embodiment of the invention includes calculating the optimal angle to maximize yield in a photonic harvesting setup with a flat solar panel and a plane mirror (reflector) with an adjustable angle (Θ). In such an embodiment, one or more sky cameras capture images I={ . . . It−2, It−1, Ipresent(t)}. For a given configuration angle Θ, the set of available (non-occluded) “T” arcs can be pre-computed via geometric view-point analysis during a system calibration phase. Additionally, a machine learning model can be learned and/or developed as a regression function (Fsky) that is pre-trained to regress to the solar irradiance components with a sector-wise spatial spread. Accordingly, in such an embodiment, [Rs_1, Rs_2, Rs_3, . . . , Rs_k]=Fsky, wherein Rs_pis the irradiance vector for an arc s_p. The hemispherical region absorbed by the one or more relevant sky cameras is divided or partitioned into non-overlapping sections that are split by cross-sectional cuts with concentric circles. Each irradiance vector (Rs_p) decouples the total irradiance into direct and diffuse components: Rs_p=[Ds_p, Dfs_p] by Fsky. As noted above, Fskyis defined as a regression function, which is a sub-routine with parameters that are typically obtained from a machine learning algorithm (hence the word pre-trained). The output of the function Fskyincludes generation of a vector R corresponding to each relevant section of the sky. The vector R is a two-tuple which contains both direct and diffused components. Hence, R is obtained by Fsky.

Further, in one or more embodiments of the invention, the available irradiance is given by RtΘi=Σk(Tk), and the problem of selecting the optimal angle is reduced to Θi, corresponding to maximum irradiance (R) and time (t). If the degrees of freedom in connection with the implementation of such an embodiment are expanded, the available arcs T become a disjoint set of arcs. Also, in such an embodiment, the optimal angle Θican be obtained by one or more linear programming (LP) methods.

FIG. 4is a flow diagram illustrating techniques according to an embodiment of the invention. As detailed herein, one or more embodiments of the invention can include, in a training phase, implementing a machine learning model that learns a function (Fsky) from sky camera input image(s), and performing an arc-sector decomposition of irradiance using measurements from a pyranometer. By way of illustration,FIG. 4depicts a processed frame402of the hemispherical region absorbed by the sky camera(s), which is divided into non-overlapping sections that are split by cross-sectional cuts with concentric circles. Information derived from processed frame402can be provided to a representation learning component404, which generates sky representation vectors406. The sky representation vector includes the internal working of Fsky. In one or more embodiments of the invention, because Fskyis a type of machine learning algorithm, the sky representation is a fixed length vector of real numbers that uniquely encodes a particular sector of the sky, generated by the algorithm corresponding to an image. The learning algorithm uses this vector to find R, the direct and diffused irradiance of the sector.

The sky representation vectors406are used by an irradiance model410, which also receives input in the form of one or more items of weather data and/or metadata408, whereby the irradiance model410generates and outputs irradiance vectors412. In one or more embodiments of the invention, the metadata408can include additional information that may be directly or indirectly relevant to the prediction and forecast of irradiance. Such metadata408can include weather data sensed separately using special sensors, such as barometric pressure, ambient temperature, wind speed, etc. Such metadata408can also include details of surrounding terrain (such as reflective terrains including snow, salt plains etc.). As also depicted inFIG. 4, the irradiance model410interacts with one or more model constraints416, which can include inputs in the form of total measured irradiance414.

By way of further illustration, in at least one embodiment of the invention, training data can include input sky images (which can be, for example, pre-processed to enhance the presence of light sources), sector-arc boundaries, total direct and diffuse irradiance measured at the point RT=[DT,DfT], and/or one or more weather measurements (temperature, wind speed, barometric pressure, etc.). Additionally, a regression is computed from a sky-representation obtained from an individual sector-arc region of the sky images and a part-irradiance vector Rs_p=[Ds_p, Dfs_p]. The regression can be constrained such that the total measured irradiance is equally partitioned into the sectors, RT=Σ(Rs).

In one or more embodiments of the invention, such a training process may be improved via the use of fine-grain training data that can include diffuse irradiance data measured with a specialized diffuse irradiance measurement device (hallowed pyranometers, etc.), and/or arc-sector specific measurements obtained from multiple sensors or a network of sky cameras.

Accordingly, in at least one embodiment of the invention, a sector-based approach can include utilizing multiple sensors and/or sky cameras, placed at appropriate distances (from each other) to measure irradiance specific to a local sector. In such an embodiment, a learning model can accept as input these individual sectors of an image, as well as one or more local weather parameters. The learning model can learn a representation of the relevant portion(s) of sky from one or more image characteristics and external weather data.

As also detailed herein, in such an embodiment, each image obtained by the sky cameras can be divided into different sectors. The ground truth for each sector may be recorded using individual sensors. Further, using a regression model, a diffuse component of solar irradiance for each sector can be calculated. Output generated by the above-noted learning model output can include sector-by-sector produced amounts of diffuse and direct solar irradiance. Based at least in part on measurements from a network of pyranometers, such an embodiment can include calculating each sector's individual diffuse and direct solar irradiance. Such calculated values can be used, for example, as the ground truth to learn and/or develop the model. Further, in such an embodiment, a machine learning model regresses towards the correct diffuse solar irradiance, wherein such a machine learning model can include one or more deep learning techniques and/or deep learning models.

Such a machine learning model can be trained for a fixed location using a training sample, and the location can be chosen such that the location encounters multiple weather conditions (for improved and/or varied training of the model). Additionally, before deploying the model, local sensed data (for a given duration of time such as, for example, multiple weeks) can be used by one or more embodiments of the invention to fine-tune the model for a current geographical location.

FIG. 5is a flow diagram illustrating techniques according to an embodiment of the invention. Step502includes calculating the area of sky covered by a set of one or more sky cameras. In one or more embodiments of the invention, the area can include a circular area which can be divided into different equidistant sectors. Further, using an individual pyranometer, such an embodiment can include calculating the diffuse and direct irradiance obtained for each sector (and using such a calculation as the ground truth to train a learning model).

Step504includes pre-processing the image(s) obtained from the sky camera(s) using one or more vision techniques. Such vision techniques can include, for example, machine learning-based methods (regression, deep neural networks, etc.) and can depend on the nature of the data available and sky camera parameters. Additionally, such vision techniques can be applied, for example, in a sector-wise fashion to localize diffused light sources in the sky.

Step506includes dividing the image(s) into different sectors, and sending the data corresponding to each sector to a machine learning model. Step508includes implementing the machine learning model (for example, a regression model) for prediction and forecasting of diffuse irradiance. In an example embodiment of the invention, given a sector of an image, the machine learning model regresses towards the diffuse solar irradiance.

Further, step510includes calculating and outputting (via the machine learning model) produced amounts of diffuse and direct irradiance for each sector of the image(s). Using such outputs, one or more embodiments of the invention can include adjusting one or more photovoltaic modules (solar panels) and/or one or more configurable add-ons in relation to the one or more sectors which produce the maximum amount of diffuse solar irradiance.

As detailed herein, diffuse irradiance sources can include multiple sources such as, for example, ground surface diffuse irradiance, which can be measured using a masking approach to record the halo effect on the corners of a sky camera lens. The light sensed from the edges can be measured by comparison with the expected irradiance found from nearby regions. During a training phase, such a measurement can be validated with deployed sensors and/or other sky cameras.

FIG. 6is a flow diagram illustrating techniques according to an embodiment of the present invention. Step602includes determining, for each of multiple portions of the sky, by using one or more machine learning algorithms, a respective level of diffuse irradiance corresponding to image data from that portion, wherein the one or more machine learning algorithms comprise at least one regression function that analyzes irradiance vectors from image data corresponding to the multiple portions of the sky, and wherein the determining comprises, for the image data corresponding to each of the multiple portions of the sky, decoupling an irradiance vector into (i) a direct irradiance component and (ii) a diffuse irradiance component. The image data can include image data attributed to at least one clear sky region and/or image data attributed to at least one sky region containing one or more clouds. Additionally, at least one embodiment of the invention can include capturing the image data via one or more sky cameras, wherein the one or more sky cameras can include (i) one or more infrared cameras, (ii) one or more near-infrared cameras, (iii) one or more thermal cameras, and/or (iv) one or more cameras having lenses with different focal lengths.

Step604includes identifying one or more portions of the image data corresponding to the multiple portions of sky image data that include a higher (for example, above a given threshold) level of diffuse irradiance, as compared to other portions of the image data.

Step606includes configuring one or more solar photovoltaic modules based at least in part on the one or more identified portions of image data that include a higher level of diffuse irradiance. At least one embodiment of the invention also includes configuring one or more add-on features to the one or more solar photovoltaic modules based at least in part on the one or more identified portions of image data that include a higher level of diffuse irradiance. The one or more add-on features to the one or more solar photovoltaic modules can include one or more reflectors and/or one or more mirrors.

Additionally, in connection with the techniques depicted inFIG. 6, at least one embodiment of the invention can include repeating (i) step602, (ii) step604, and (iii) step606for one or more additional iterations. Repeating can include, for example, repeating (i) step602, (ii) step604, and (iii) step606for one or more additional iterations in accordance with a pre-determined temporal schedule.

The techniques depicted inFIG. 6can also include predicting, based at least in part on the determined level of diffuse irradiance corresponding to image data from each of the multiple portions of the sky, a respective future level of diffuse irradiance corresponding to image data from each of the multiple portions of the sky. One or more embodiments of the invention can also include configuring one or more solar photovoltaic modules based at least in part on (i) the one or more identified portions of image data that include a higher level of diffuse irradiance and (ii) the predicted future level of diffuse irradiance corresponding to image data from each of the multiple portions of the sky. Such an embodiment can additionally include configuring one or more add-on features to the one or more solar photovoltaic modules based at least in part on (i) the one or more identified portions of image data that include a higher level of diffuse irradiance and (ii) the predicted future level of diffuse irradiance corresponding to image data from each of the multiple portions of the sky.

Also, the techniques depicted inFIG. 6can include determining, using one or more machine learning algorithms, a respective level of diffuse irradiance corresponding to each of one or more terrain elements. Further, at least one embodiment of the invention includes configuring one or more solar photovoltaic modules based at least in part on (i) the one or more identified portions of image data that include a higher level of diffuse irradiance and (ii) the determined level of diffuse irradiance corresponding to each of the one or more terrain elements.

Also, an additional embodiment of the invention includes generating a machine learning model for determining irradiance levels, wherein the machine learning model is based at least in part on (i) sky image data captured over a temporal period and (ii) one or more weather conditions, corresponding to the sky image data, sensed over the temporal period. Such an embodiment can also include capturing, using multiple cameras, image data specific to one or more regions of sky, sensing one or more transient weather conditions at geographic regions corresponding to the one or more regions of sky, and applying the machine learning model to (i) the captured image data specific to the one or more regions of sky and (ii) the one or more transient weather conditions sensed at the geographic regions corresponding to the one or more regions of sky. Further, such an embodiment can include outputting, based on implementation of the machine learning model, (i) sector-by-sector levels of produced diffuse irradiance and (ii) sector-by-sector levels of produced direct irradiance; and configuring one or more solar photovoltaic modules based at least in part on (i) the sector-by-sector levels of produced diffuse irradiance and (ii) the sector-by-sector levels of produced direct irradiance.

Additionally, an embodiment of the present invention can make use of software running on a computer or workstation. With reference toFIG. 7, such an implementation might employ, for example, a processor702, a memory704, and an input/output interface formed, for example, by a display706and a keyboard708. The term “processor” as used herein is intended to include any processing device, such as, for example, one that includes a CPU (central processing unit) and/or other forms of processing circuitry. Further, the term “processor” may refer to more than one individual processor. The term “memory” is intended to include memory associated with a processor or CPU, such as, for example, RAM (random access memory), ROM (read only memory), a fixed memory device (for example, hard drive), a removable memory device (for example, diskette), a flash memory and the like. In addition, the phrase “input/output interface” as used herein, is intended to include, for example, a mechanism for inputting data to the processing unit (for example, mouse), and a mechanism for providing results associated with the processing unit (for example, printer). The processor702, memory704, and input/output interface such as display706and keyboard708can be interconnected, for example, via bus710as part of a data processing unit712. Suitable interconnections, for example via bus710, can also be provided to a network interface714, such as a network card, which can be provided to interface with a computer network, and to a media interface716, such as a diskette or CD-ROM drive, which can be provided to interface with media718.

A data processing system suitable for storing and/or executing program code will include at least one processor702coupled directly or indirectly to memory elements704through a system bus710. The memory elements can include local memory employed during actual implementation of the program code, bulk storage, and cache memories which provide temporary storage of at least some program code in order to reduce the number of times code must be retrieved from bulk storage during implementation.

Input/output or I/O devices (including, but not limited to, keyboards708, displays706, pointing devices, and the like) can be coupled to the system either directly (such as via bus710) or through intervening I/O controllers (omitted for clarity).

Network adapters such as network interface714may also be coupled to the system to enable the data processing system to become coupled to other data processing systems or remote printers or storage devices through intervening private or public networks. Modems, cable modems and Ethernet cards are just a few of the currently available types of network adapters.

As used herein, including the claims, a “server” includes a physical data processing system (for example, system712as shown inFIG. 7) running a server program. It will be understood that such a physical server may or may not include a display and keyboard.

Additionally, it is understood in advance that implementation of the teachings recited herein are not limited to a particular computing environment. Rather, embodiments of the present invention are capable of being implemented in conjunction with any type of computing environment now known or later developed.

Characteristics are as follows:

Service Models are as follows:

Deployment Models are as follows:

Virtualization layer70provides an abstraction layer from which the following examples of virtual entities may be provided: virtual servers71; virtual storage72; virtual networks73, including virtual private networks; virtual applications and operating systems74; and virtual clients75. In one example, management layer80may provide the functions described below. Resource provisioning81provides dynamic procurement of computing resources and other resources that are utilized to perform tasks within the cloud computing environment. Metering and Pricing82provide cost tracking as resources are utilized within the cloud computing environment, and billing or invoicing for consumption of these resources.

In one example, these resources may include application software licenses. Security provides identity verification for cloud consumers and tasks, as well as protection for data and other resources. User portal83provides access to the cloud computing environment for consumers and system administrators. Service level management84provides cloud computing resource allocation and management such that required service levels are met. Service Level Agreement (SLA) planning and fulfillment85provide pre-arrangement for, and procurement of, cloud computing resources for which a future requirement is anticipated in accordance with an SLA.

Workloads layer90provides examples of functionality for which the cloud computing environment may be utilized. Examples of workloads and functions which may be provided from this layer include: mapping and navigation91; software development and lifecycle management92; virtual classroom education delivery93; data analytics processing94; transaction processing95; and irradiance-based photovoltaic module configuration96, in accordance with the one or more embodiments of the present invention.

At least one embodiment of the present invention may provide a beneficial effect such as, for example, dividing a circular area into different equidistant sectors for predicting diffuse irradiance.