DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-16, 18-21 are pending in the application.
Examiner’s Note: The examiner has cited particular passages including column and line numbers, paragraphs as designated numerically and/or figures as designated numerically in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claims, other passages, paragraphs and figures of any and all cited prior art references may apply as well. It is respectfully requested from the applicant, in preparing an eventual response, to fully consider the context of the passages, paragraphs and figures as taught by the prior art and/or cited by the examiner while including in such consideration the cited prior art references in their entirety as potentially teaching all or part of the claimed invention. MPEP 2141.02 VI: “PRIOR ART MUST BE CONSIDERED IN ITS ENTIRETY, INCLUDING DISCLOSURES THAT TEACH AWAY FROM THE CLAIMS."
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 01/15/2020 and 09/21/2023 was filed after the mailing date of the first office action. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 10 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 10 recites a limitation "determining the motion vector field comprises application of one of: a Lucas-Kanade algorithm; Farneback's Polynomial Expansion method; the Anisotropic Diffusion method; and, the Horn-Schunk method". The plain meaning of phrase "at least one of A and B" is "at least one of A and at least one of B" (for more details please see Ex parte Jung, 2016-008290 (PTAB Mar. 22, 2017) and/or SuperGuide Corp. v. DirecTV Enters., Inc., 358 F.3d 870 (Fed. Cir. 2004)). It is hard to comprehend that system is configured to determining the motion field using multiple algorithms/methods simultaneously. For continuing examination purpose, this limitation in the claim has been construed as at least one of the fallowing: a Lucas-Kanade algorithm; Farneback's Polynomial Expansion method; the Anisotropic Diffusion method; [[and]], or the Horn-Schunk method", as describe in the application description. [0070 - The motion vector field may be determined using any suitable algorithm, such as the Lucas-Kanade method, Farneback's Polynomial Expansion method, the Anisotropic Diffusion method, or the Horn-Schunk method.]
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-19, 21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Regarding claims 1 and 18, the claims recite a computer implement method or instruction to perform the method of determining predicted power generation of renewable energy comprises a series of steps and, therefore, is a process, which are statutory categories of invention.
Regarding claims 19 and 21, the claim recites a system comprising one or more computer processors for determining predicted power generation of a renewable energy installation, which is a mechanical and/or electrical device such as a general-purpose computer. Thus, the claim is to a manufacture or a machine, which are statutory categories of invention.
Step 2A Prong one: The claim(s) recite(s) the steps of
receiving current power generation data indicative of a current power generation value for each of one or more neighbouring renewable energy installations;
determining, based on the received current power generation data, a current data map indicative of current power generation values across an area including the renewable energy installation and the one or more neighboring renewable energy installations;
retrieving a previous data map indicative of power generation values across the area at a previous time, and
determining, based on the previous and current data maps, a future data map indicative of power generation values across the area at a future time; and,
determining a predicted power generation value of the renewable energy installation at the future time based on the determined future data map.
The limitation (a) to (e) as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind with the help of pen and paper but for the recitation of generic computer components (one or more computer processors). For example, steps (a) receiving, (b) determining in the context of this claim encompasses recording the power output from nearby solar panels, draw a simple map on paper with locations of neighboring installations, and label each location with the corresponding power output. Steps (c) retrieving, (d) determining in the context of this claim encompasses take a previously (yesterday) draw map compare yesterday’s map to today’s map, make an estimate of tomorrow’s power output at each location by projecting trends, and draw a new map on paper with these projected values. Essentially, performing mental forecasting using simple arithmetic or trend analysis. Steps (e) determining in the context of this claim encompasses look at the future data map and read off the project value for the renewable energy installation of interest. Therefore, if the claims limitation, under its broadest reasonable interpretation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of an abstract ideas. Accordingly, the claims recite an abstract idea.
Step 2A Prong Two: Besides the abstract ideas, the claims recite “one or more computer processors” being configured to perform the steps (a) to (e). However, it is merely a tool that is used to obtain the power output data, determine/retrieve map, and perform projection. The processors are recited so generically that is represents no more than mere instructions to apply to judicial exceptions on a computer. As such, it is nothing more than an attempt to generally link the used of judicial exceptions to the technological environment of a computer. Even when viewed in combination, the additional element does not integrate the recited judicial exception into a practical application and the claim is directed to the judicial exception.
Step 2B: The claim as a whole does not amounts to significantly more than the recited exception. The claim has an additional elements of a processor, which is configured to perform limitations (a) through (d). As explained previously, the processor is at best the equivalent of merely adding the words “apply it” to the judicial exception. Mere instructions to apply an exception cannot provide an inventive concept. The steps of collecting data, comparing data sets, and forecasting value based on those data are routine and conventional in the field of data analytics. There is no recited improvement to data acquisition hardware, data structures, processing architecture, or machine operation. Even when considered in combination, these additional elements represent mere instructions to apply an exception and insignificant extra-solution activity, which do not provide an inventive concept. The claim is not eligible.
Regarding claim 2-16 depend from claim 1 and therefore incorporate the same abstract idea. The additional limitation recited in claims 2-16 merely add further mathematical operation, data manipulation rules, or information processing steps, such as:
Retrieving history data (claims 2, 4, 5)
Normalizing, dividing, or multiplying values (claims 2, 3, 5, 6)
Interpolating data and generating heat maps (claims 7, 8)
Computing motion vector fields and translating maps (claims 9-10)
Filtering installations by distance or angle (claims 11-14)
Performing simple-time-interval arithmetic (claim 15)
Outputting a signal (claim 16)
These additional elements constitute mathematical concepts, mental processes, or data-analysis steps, all of which are abstract ideas. These claims do not recite any improvement to computer functionality, any particular machine, or any unconventional technical solution that would integrate the abstract idea into a practical application. Furthermore, the added elements are well-understood, routine, and conventional data-processing steps performed on generic computer systems and therefore fail to supply an inventive concept under step 2B. Accordingly, claims 2-16 do not overcome the ineligibility of claim 1, and are likewise rejected under 35 U.S.C 101.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-7, 11, 15, 18-19, 21 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Charalambides et al. WO. 2016146788 A1 (“Charalambides”)
Regarding claim 1, Charalambides disclose a computer-implemented method for determining predicted power generation of a renewable energy installation, the method comprising:
receiving current power generation data indicative of a current power generation value for each of one or more neighbouring renewable energy installations;
The monitoring means comprises a plurality of monitors 10, each of which is connected to one or more solar power generators 100, and is configured to measure the power output of each generator in the network. The measured power output is sent to the database 20 and to the processing server 30 by the plurality of monitors 10. The monitors 10 may be configured to measure the power output in a continuous manner, or otherwise may measure the power output at a predetermined sample rate. The time between measurements may be in the range of o to 5 minutes and, in some embodiments, may be up to 15 minutes. [page 4]
determining, based on the received current power generation data, a current data map indicative of current power generation values across an area including the renewable energy installation and the one or more neighboring renewable energy installations;
The database 20 may also receive and store data which is generated by the processing server 30 in relation to the forecasting process. With reference to Figure 2, the processing server 30 according to the first embodiment is shown, which comprises a normalisation unit 31 for generating a power attenuation map, a motion estimation unit 32 for calculating a corresponding dynamic flow map for attenuation and a forecasting unit 33 for predicting the expected power output of the network of solar power generators 100. The normalisation unit 31 is configured to generate a power attenuation map using the measured power output of the network received from the plurality of monitors 10. [page 5]
retrieving a previous data map indicative of power generation values across the area at a previous time, and
determining, based on the previous and current data maps, a future data map indicative of power generation values across the area at a future time; and
The motion estimation unit 32 is configured to calculate a dynamic flow map for power attenuation representing, in terms of attenuation from the maximum power output, the change in power output of the network over time. The motion estimation unit 32 calculates the dynamic flow map for power attenuation from a plurality of power attenuation maps generated sequentially by the normalisation unit 31. The sequentially generated power attenuation maps may be stored in the database 20. [page 7]
determining a predicted power generation value of the renewable energy installation at the future time based on the determined future data map.
Based on the calculated dynamic flow map for power attenuation, the forecasting unit 33 generates a predicted set of flow vectors for a future point in time which can be applied to the most recent power attenuation map to generate an expected power attenuation map for that point in time. [page. 8]
The forecasting unit 33 applies the expected power attenuation map to the maximum power output for the network of solar power generators 100 at the corresponding point in the future, in order to predict the expected power generation for the network of solar power generators 100. In this way, the present invention provides an accurate forecast for the power output of a network of solar power generators 100 which is based only on the measured power output of the network. [page 9]
Regarding claim 2, Charalambides discloses for each of the neighbouring renewable energy installations: retrieving historical power generation data [maximum power output of the solar power generator] indicative of one or more historical power generation values for the neighbouring renewable energy installation; and, normalising the received current power generation data against the retrieved historical power generation data, wherein the current data map is determined based on the normalised current power generation data.
The normalisation unit 31 is configured to generate a power attenuation map using the measured power output of the network received from the plurality of monitors 10. The measured power output is received by the normalisation unit 31 and is converted to a normalised power output based on a maximum power output for the network.
The maximum power output stored in the database 20 is a function of time, which accounts for the position of the sun to provide an expected value for the power output at any given time of day on any given day of the year. The database 20 may therefore store up to 365 daily maximum power output graphs for each PV or each monitor in the network. The maximum power output may depend on a location of the solar power generators 100 in the network and additionally on configuration information such as the size, orientation, interconnections, installed capacity or inverter efficiency of a solar power generator.
A system 1 according to this embodiment of the invention therefore requires no external data beyond the measure power output in order to calculate a maximum output power and calculate the normalised power output of the network. Accordingly, the power attenuation map for the network can be generated by the normalisation unit 31 using the measured power output as the only exogenous data input. [See page 4 and 5]
Regarding claim 3, Charalambides discloses normalising the received current power generation data comprises dividing the current power generation value by a value representative of the one or more historical power generation values for the neighbouring renewable energy installation,
After the optional configuration phase, the measured power output of the network is normalised with respect to a stored maximum power output of the network (S4), to calculate a normalised power output of the network of solar power generators. A power attenuation map is generated (S5), based on the difference between the normalised power output and the maximum power output. [See page 9 – attenuation = actual power out/max power output]
Regarding claim 4, Charalambides discloses the retrieved historical power generation data comprises power generation values for the neighbouring renewable energy installation at a same time on one or more previous days as a current time [See page 5].
Regarding claim 5, Charalambides discloses the future data map comprises normalised power generation values, and wherein determining the predicted power generation value comprises: retrieving historical power generation data indicative of one or more historical power generation values [maximum power output] for the renewable energy installation; obtaining the normalised predicted power generation value of the renewable energy installation at the future time from the determined future data map; and, determining the predicted power generation value from the normalised predicted power generation value against the retrieved historical power generation data for the renewable energy installation.
The motion estimation unit 32 determines the displacement and deformation of identified contours between sequentially generated power attenuation maps, in order to calculate a dynamic flow map for power attenuation across the network of solar power generators 100. The motion estimation unit 32 is configured to calculate the dynamic flow map based on at least two sequentially generated power attenuation maps. [page 7]
The forecasting unit 33 applies the expected power attenuation map to the maximum power output for the network of solar power generators 100 at the corresponding point in the future, in order to predict the expected power generation for the network of solar power generators 100. In this way, the present invention provides an accurate forecast for the power output of a network of solar power generators 100 which is based only on the measured power output of the network. [page 9]
Regarding claim 6, Charalambides discloses determining the predicted power generation value comprises multiplying the normalised predicted power generation value by a value representative of the one or more historical power generation values for the renewable energy installation, inherent since attenuation = actual power out/max power output. Thus, predicted power value = predicted attenuation value X max power output].
Regarding claim 7, Charalambides discloses determining the current data map comprises interpolating the data indicative of current power generation values across the area, optionally performing the interpolation across a grid covering the area.
An initial estimate may be made using interpolation, and improved by iteration over time with additional measurements from the connected monitor. [page 6]
The motion estimation unit 32 determines the displacement and deformation of identified contours between sequentially generated power attenuation maps, in order to calculate a dynamic flow map for power attenuation across the network of solar power generators 100. [page 8; and claim 8]
Regarding claim 9, Charalambides discloses determining the future data map comprises: determining a motion vector field of the area based on the previous and current data maps; and, translating the current data map along the determined motion vector field to the future time.
In some embodiments, the contour motion algorithm comprises a block matching process applied to a plurality of sequentially generated power attenuation maps. Each power attenuation map is segmented into several small regions (blocks), the size of which is defined according to the spatial resolution of the image. The contour motion algorithm defines the displacement of each block between sequential power attenuation maps and calculates a corresponding velocity for each block. The displacement for a block is defined by comparing the block from a first map to a plurality of blocks having a corresponding size in a subsequent map. A maximum search distance may be defined according to the temporal resolution of the measured power output data, to improve accuracy of the algorithm. A metric for the best match within the allowed search distance can be defined by the minimisation of a cost function. Exemplary metrics include the sum of squared differences, mean squared error, cross correlation coefficient and the absolute value of differences.
The motion estimation unit 32 determines the displacement and deformation of identified contours between sequentially generated power attenuation maps, in order to calculate a dynamic flow map for power attenuation across the network of solar power generators 100. [pages 7-8]
Regarding claim 11, Charalambides discloses each of the neighbouring renewable energy installations are within a prescribed distance from the renewable energy installation [page 4 - the system of the present invention is adapted to accumulate data from a geographic region with a number of solar power generators having different sizes and configurations].
Regarding claim 15, Charalambides discloses a difference between the previous time and a current time is equal to a difference between the current time and the future time [See page 4, 8-9].
Regarding claim 18, Charalambides disclose a non-transitory, computer-readable storage medium storing instructions thereon that when executed by a processor cause the processor to perform a method according to claim 1 [See fig. 2 and 4].
Regarding claim 19, it is directed to a system to implement the method of steps as set forth in claim 1. Therefore, it is rejected on the same basis as set forth hereinabove.
Regarding claim 21, Charalambides disclose a renewable energy installation comprising a system according to claim 19 [SEE fig. 1].
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 8, 10, 12-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Charalambides as applied to claim 1+ 7 or 1+9 above.
Regarding claim 8, Charalambides teaches the current map obtained from the interpolated data indicative of current power generation values [SEE discussion in claim 7]. Charalambides does not expressly teach the current data map is a heat map. However, it is obvious to one of ordinary skill in the art to represent spatially varying attenuation values of Charalambides as a heat map, since heat maps are a well-known and conventional visualization for displaying continuous spatial data such as temperature, irradiance, density, or intensity distribution.
Regarding claim 10, Charalambides does not teach determining the motion vector field comprises application of one of: a Lucas-Kanade algorithm; Farneback's Polynomial Expansion method; the Anisotropic Diffusion method; and, the Horn-Schunk method. However, it would have been obvious to one of ordinary skill in the art at the time of the invention was filed to determine the motion vector field using any of the well-known optical-flow algorithms such as Lucas-Kanade algorithm, Farneback's Polynomial Expansion method, the Anisotropic Diffusion method, or the Horn-Schunk method, the methods were routinely used and widely taught in the art for generating motion vector fields from sequential image or data maps. Once Charalambides teaches computing a motion vector field between previous and current data maps, one of ordinary skill would have recognized that any conventional optical-flow technique could be applied to obtain such a field, and that the choice among these well-established algorithms is simply a matter of design choice, routine optimization, or predictable using of prior-art technique to achieve the expected result.
Regarding claims 12-14, Charalambides teaches receiving neighbouring renewable energy installation, constructing a spatial map of current power generation, retrieving past maps, generating future map, and predicting future power output of a target renewable installation. The only distinctions alleged in claims 12-14 relate to specific geometric criteria or spatial filtering used before creating the current data map. These additional steps amount to routine, predictable, and non-functional modifications of how neighbouring installations are selected or validated; Charalambides already teaches that selecting neighbouring renewable energy installation, defining spatial boundaries, and determining whether environmental data is relevant are obvious to one of skill in the art.
Claim 12 recites prior to determining the current data map: for each of the neighbouring renewable energy installations, forming a virtual line between the neighbouring renewable energy installation and the renewable energy installation; and, determining the current data map if each of the angles between adjacent virtual lines is below a prescribed threshold angle. Charalambides already teaches identifying neighbouring renewable energy installation and arranging them spatially to model the propagation of weather effects. Determining whether neighbouring renewable energy installation are suitably distributed around the target installation is an obvious geometric pre-check that does not change the basic data-mapping function. Use of angle thresholds is merely one of numerous predictable alternatives for verifying adequate spatial distribution that provides no new function and yields the same predicted power output as Charalambides. Thus, the claimed angle filter represents only a matter of designer preference, not a patentable distinction.
Claim 13 recites prior to determining the current data map: fitting a virtual polygon between the neighbouring renewable energy installations, each neighbouring renewable energy installation being a vertex of the virtual polygon, and each pair of adjacent neighbouring renewable energy installations being connected by an edge of the virtual polygon; identifying the edge closest to the renewable energy installation; and, determining the current data map if a distance between the renewable energy installation and a closest point of the identified edge to the renewable energy installation is greater than a prescribed distance.
Again, Charalambides already requires determining the relative spatial arrangement of neighbouring installations. The claimed polygon test is simply another mathematical formalism for assessing the density and placement of neighbouring points. Using a convex hull, polygon boundary, or minimal distance metric are all interchangeable, routine geometric techniques well known in spatial modeling. Nothing in claim 13 produces a new or unexpected result vs Charalambides’ neighbouring renewable energy installation and spatial-mapping operations. Thus, this limitation is also an obvious design choice.
Claim 14 recites receiving a direction of travel of weather conditions, optionally a wind direction, in the vicinity of the renewable energy installation; and, determining the current data map if the direction of travel is different from a direction from the closest point of the identified edge to the renewable energy installation. Charalambides already teaches accounting for wind direction (external factors may be used, such as the prevailing wind direction for the region, to further improve the accuracy of the contour motion algorithm) and propagation of weather patterns when constructing predictive maps. Using wind direction to determine whether a spatial distribution filters should be applied is a routine conditional check that a skill person in the art would find obvious. Selecting whether to apply a geometric validation based on alignment or misalignment with weather direction is simply one predictable rule among many possible heuristics. As such, this condition does not contribute any technical improvement beyond Charalambides’ existing weather dependent mapping. It is another non-functional refinement falling under design choice.
In summary, claims 12-14 merely specify alternative geometric or directional filtering heuristics for determining whether neighbouring renewable energy installation should be used when building the current map. These limitations do not alter the operation of results of Charalambides which to accurately predict future power output of renewable energy installation. Therefore, when Charalambides already teaches selecting neighbouring renewable energy installation for constructing spatial prediction maps, one of ordinary skill in the art would have found the claimed geometric/directional filters to be obvious as a matter of designer choice, rendering claims 12-14 unpatentable.
Claim(s) 16 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Charalambides as applied to claim 1 or 19 above, and further in view of Guha et al. US Pub. No. 2015/0186905 (“Guha”).
Regarding claim 16, Charalambides teaches an invention provides accurate forecasting, therefore avoids the need for compensation using base load power plants, which leads to a reduction in C0.sub.2 omissions across the electricity power grid. However, Charalambides does not expressly teaches outputting a signal indicative of the predicted power generation value optionally comprising transmitting a control signal to control operation of one or more appliances powered by the renewable energy installation based on the predicted power generation value.
Guha teaches a system and method for managing and forecasting power from renewable energy sources, such as solar and wind power. Specifically, Guha teaches outputting a signal indicative of the predicted power generation value optionally comprising transmitting a control signal to control operation of one or more appliances powered by the renewable energy installation based on the predicted power generation value.
[0032] Further, according to an exemplary embodiment, performance of the tasks (including when the tasks are performed) is automated. For instance, the present techniques make use of technology that permits tasks such as setting a thermostat, turning on/off an appliance, etc. to be performed automatically under the control of a controller 308.
[0036] Given the management system shown in FIG. 3, a methodology 400 is now provided for managing use of energy generated by renewable energy sources, such as solar/wind power. The steps of methodology 400 may be performed by controller 308 (see FIG. 3). As highlighted above, an apparatus that may be configured to serve as controller 308 is provided in FIG. 6, described below.
[0037] In step 402, the controller automatically makes a list (or schedule) of tasks that have to be performed in the building. As described above, data obtained from the sensors can alert the controller as to what tasks need to be performed. One or more parameters may be associated with each of the tasks, such as how much power is necessary to perform the task and a timeframe--for example, if a given task would require 2 kilowatt hours (kWh) for 2 hours, and if the solar forecasting predicts that this amount of energy would be available for only 1 hour, then another task should be scheduled that would consume less energy.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to modify the method of Charalambides with the step of outputting a signal indicative of the predicted power generation value optionally comprising transmitting a control signal to control operation of one or more appliances powered by the renewable energy installation based on the predicted power generation value of Guha. The motivation for doing so would has been to improve the effective used of the availability of the renewable energy source without require additional energy from the utility companies. Thus, reduce cost.
Regarding claim 20, See discussion in claim 16.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US Pub. No. 2014/0046610 to Bamberger et al. teach a method for improving the usability of photovoltaic installations (PV installations) by taking account of shading information of adjacent PV installations for forecasting the power output by a relevant PV installation is provided. In particular, cloud movements and cloud shapes are taken into account. This improves the accuracy of the forecast. Specifically, Bamberger et al. show, in FIG. 1, a schematic diagram with one PV installation 101 and several adjacent PV installations 102 to 107. Each one of the PV installations 101 to 107 comprises a measurement zone, on the basis of which a shading can be determined. The measurement zone preferably relates to the solar modules: the shading can be deduced on the basis of the variation in the power output. Thus, an individual solar module or a group of solar modules can be used as a measurement zone. It is also possible for several (e.g. small) PV installations to be combined to form a single measurement zone; correspondingly it is possible for large PV installations to be employed as a single measurement zone or even to provide several measurement zones.
US Pub. No. 2015/0177415 to Bing provides a system 100 for predicting direct normal, diffuse horizontal, and global horizontal irradiance. The system is defined over a geographic region of interest 120. The region of interest 120 is subdivided into a gridded pattern 110 comprised of grid cells. Each grid cell 130 in the system 100 includes any or all of one or more of an irradiance monitoring device which can measure direct normal irradiance and diffuse horizontal irradiance and which can communicate with a central computer, and or groups of two or more of any combination of the following which have different tilt and or azimuth angles and which are in close physical proximity and which have communications capability to a central computer, of an irradiance monitoring device, a PV generating unit which can measure and report its energy production, a kilowatt-hour meter which measures and reports the energy production of a PV generating unit, a data acquisition system which measures and reports the ac production of a PV generating unit, and or a data acquisition system which measures and reports the dc production of a PV generating unit.
US Pub. No. 2013/0066569 to Sato teaches a power generation predicting apparatus in which an estimator estimates a maximum power generation amount capable of generating by a photovoltaic power generation system at each time, a first estimating unit estimates a power generation inhibitor total amount being a total of power generation inhibiting substances which are present until a solar light reaches the system, at each time, a weight calculating unit calculates a solar radiation weight for each of three-dimensional cells at each time, based on a straight line passing through the cells forming a three-dimensional space above a prediction target area at which the system is placed, a second estimating unit estimates a power generation inhibitor amount at a prediction target time for each of the cells, and a predicting unit calculates a power generation inhibitor total amount at the prediction target time, and predicts a power generation amount at the prediction target time.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to VINCENT HUY TRAN whose telephone number is (571)272-7210. The examiner can normally be reached on M-F 7:00-4:00.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Thomas C Lee can be reached on 571-272-3667. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/VINCENT H TRAN/Primary Examiner, Art Unit 2115