Derivation of Solar Insolation Estimates from LiDAR

This Honors Thesis describes the methodology that was used from May 2010 to December 2010 for deriving solar insolation estimates for the University of Colorado at Boulder campus from Light Detection And Ranging (LiDAR) data. Background is given on the LiDAR data set used, including acquisition considerations and the properties of the data set itself. The primary method used to derive solar insolation estimates of campus was the generation of first-return canopy Digital Elevation Models (DEMs) using ENVI, followed by slope and aspect calculations using the open-source Geographic Information System (GIS) GRASS. The slope and aspect raster tiles were used to derive solar insolation estimates using the r.sun GRASS module, and an extraction of campus building rooftops was accomplished using existing vector campus GIS data sets. The reasons and constraints that led to the development of this methodology are discussed and possible sources of error are considered. Finally, the findings and implications of this study are presented and additional steps to reduce error in future work are explored

Non-intrusive load monitoring solutions for low- and very low-rate granularity

Strathclyde theses - ask staff. Thesis no. : T15573Large-scale smart energy metering deployment worldwide and the integration of smart meters within the smart grid are enabling two-way communication between the consumer and energy network, thus ensuring an improved response to demand. Energy disaggregation or non-intrusive load monitoring (NILM), namely disaggregation of the total metered electricity consumption down to individual appliances using purely algorithmic tools, is gaining popularity as an added-value that makes the most of meter data.In this thesis, the first contribution tackles low-rate NILM problem by proposing an approach based on graph signal processing (GSP) that does not require any training.Note that Low-rate NILM refers to NILM of active power measurements only, at rates from 1 second to 1 minute. Adaptive thresholding, signal clustering and pattern matching are implemented via GSP concepts and applied to the NILM problem. Then for further demonstration of GSP potential, GSP concepts are applied at both, physical signal level via graph-based filtering and data level, via effective semi-supervised GSP-based feature matching. The proposed GSP-based NILM-improving methods are generic and can be used to improve the results of various event-based NILM approaches. NILM solutions for very low data rates (15-60 min) cannot leverage on low to highrates NILM approaches. Therefore, the third contribution of this thesis comprises three very low-rate load disaggregation solutions, based on supervised (i) K-nearest neighbours relying on features such as statistical measures of the energy signal, time usage profile of appliances and reactive power consumption (if available); unsupervised(ii) optimisation performing minimisation of error between aggregate and the sum of estimated individual loads, where energy consumed by always-on load is heuristically estimated prior to further disaggregation and appliance models are built only by manufacturer information; and (iii) GSP as a variant of aforementioned GSP-based solution proposed for low-rate load disaggregation, with an additional graph of time-of-day information.Large-scale smart energy metering deployment worldwide and the integration of smart meters within the smart grid are enabling two-way communication between the consumer and energy network, thus ensuring an improved response to demand. Energy disaggregation or non-intrusive load monitoring (NILM), namely disaggregation of the total metered electricity consumption down to individual appliances using purely algorithmic tools, is gaining popularity as an added-value that makes the most of meter data.In this thesis, the first contribution tackles low-rate NILM problem by proposing an approach based on graph signal processing (GSP) that does not require any training.Note that Low-rate NILM refers to NILM of active power measurements only, at rates from 1 second to 1 minute. Adaptive thresholding, signal clustering and pattern matching are implemented via GSP concepts and applied to the NILM problem. Then for further demonstration of GSP potential, GSP concepts are applied at both, physical signal level via graph-based filtering and data level, via effective semi-supervised GSP-based feature matching. The proposed GSP-based NILM-improving methods are generic and can be used to improve the results of various event-based NILM approaches. NILM solutions for very low data rates (15-60 min) cannot leverage on low to highrates NILM approaches. Therefore, the third contribution of this thesis comprises three very low-rate load disaggregation solutions, based on supervised (i) K-nearest neighbours relying on features such as statistical measures of the energy signal, time usage profile of appliances and reactive power consumption (if available); unsupervised(ii) optimisation performing minimisation of error between aggregate and the sum of estimated individual loads, where energy consumed by always-on load is heuristically estimated prior to further disaggregation and appliance models are built only by manufacturer information; and (iii) GSP as a variant of aforementioned GSP-based solution proposed for low-rate load disaggregation, with an additional graph of time-of-day information

Hybrid Black-box Solar Analytics and their Privacy Implications

The aggregate solar capacity in the U.S. is rising rapidly due to continuing decreases in the cost of solar modules. For example, the installed cost per Watt (W) for residential photovoltaics (PVs) decreased by 6X from 2009 to 2018 (from $8/W to$1.2/W), resulting in the installed aggregate solar capacity increasing 128X from 2009 to 2018 (from 435 megawatts to 55.9 gigawatts). This increasing solar capacity is imposing operational challenges on utilities in balancing electricity\u27s real-time supply and demand, as solar generation is more stochastic and less predictable than aggregate demand. To address this problem, both academia and utilities have raised strong interests in solar analytics to accurately monitor, predict and react to variations in intermittent solar power. Prior solar analytics are mostly white-box approaches that are based on site-specific information and require expert knowledge and thus do not scale, recent research focuses on black-box approaches that use training data to automatically learn a custom machine learning (ML) model. Unfortunately, this approach requires months-to-years of training data, and often does not incorporate well-known physical models of solar generation, which reduces its accuracy. Instead, in this dissertation, we present a hybrid black box approach that can achieve the best of both to solar analytics. Our hypothesis is that the hybrid black-box approach can enable a wide range of accurate solar analytics, including modeling, disaggregation, and localization, with limited training data and without knowledge of key system parameters by integrating black-box machine learning approaches with white-box physical models. In evaluating our hypothesis, we make the following contributions: (Mostly) ML black-box Solar Modeling. To get benefits from both of ML and physical approaches, we present a configurable hybrid black-box ML approach that combines well-known relationships from physical models with unknown relationships learned via ML. Rather than manually determining values for physical model parameters, our approach automatically calibrates them by finding values that best to the data. This calibration requires much less data (as few as 2 datapoints) than training an ML model. And we show that our hybrid approach significantly improves solar modeling accuracy. (Mostly) Physical black-box Solar Modeling. The physical model used in the hybrid model above performs significantly worse than other approaches. To determine the primary source of this inaccuracy, we conduct a large-scale data analysis and show that the only weather metrics that affect solar output are temperature and cloud cover, and then derive a new physical model that accurately quantify cloud cover\u27s effect on solar generation at all sites. We then enhance our physical model with an ML model that learns each site\u27s unique shading effect. And we show that the hybrid modeling yields higher accuracy than current state-of-the-art ML approaches. We also identify a universal weather-solar effect that has not been articulated before and is broadly applicable to other solar analytics. Solar Disaggregation. Solar forecast models require historical solar generation data for training. Unfortunately, pure solar generation data is often not available, as the vast majority of small-scale residential solar deployments (\u3c10kW) are Behind the Meter (BTM) , such that smart meter data exposed to utilities represents only the net of a building\u27s solar generation and its energy consumption. To address this problem, we design SunDance, a black-box\u27\u27 system that leverages the clear sky maximum solar generation model, and the universal weather-solar effect from the hybrid black-box models above. We show that SunDance can accurately disaggregate solar generation from net meter data without access to a building\u27s pure solar generation data for training. Solar-based Localization. The energy data produced by solar-powered homes is considered anonymous and usually publicly available if it is not associated with identifying account information, e.g., a name and address. Our key insight is that solar energy data is not anonymous: every location on Earth has a unique solar signature, and it embeds detailed location information. We then design SunSpot to localize the source of solar generation data and show that SunSpot is able to localize a solar-powered home within 500 meters and 28 kilometers radius for per-second and per-minute resolution. Weather-based Localization. However, the above solar-based localization has a fundamental limit due to Earth\u27s rotation. To further localize towards a specific home, we identify another key insight: every location on Earth has a distinct weather signature that uniquely identifies it. Interestingly, we find that localizing coarse (one-hour resolution) solar data using weather signature is more accurate than localizing solar data (one minute or one second resolution) using its solar signature. Both of SunSpot and Weatherman expose a new serious privacy threat from energy data, which has not been presented in the past

Calibration Uncertainty for Advanced LIGO's First and Second Observing Runs

Calibration of the Advanced LIGO detectors is the quantification of the detectors' response to gravitational waves. Gravitational waves incident on the detectors cause phase shifts in the interferometer laser light which are read out as intensity fluctuations at the detector output. Understanding this detector response to gravitational waves is crucial to producing accurate and precise gravitational wave strain data. Estimates of binary black hole and neutron star parameters and tests of general relativity require well-calibrated data, as miscalibrations will lead to biased results. We describe the method of producing calibration uncertainty estimates for both LIGO detectors in the first and second observing runs.Comment: 15 pages, 21 figures, LIGO DCC P160013

Optimal water meter selection system

The comparison of the particular accuracy envelope of a water meter with a consumer's diurnal demand pattern by means of a common reference facilitates the optimal selection of water meters. The accuracy curve and envelope of a new water meter is governed by the type of water meter and relevant standards. Water demand patterns vary with time, period, seasons, consumers and combinations of these factors. The classical accuracy envelope and demand pattern are not directly comparable, and require a common comparison reference. The relative frequency of the volume of water passing through a meter at various flow rates and the weighted accuracies of these measured volumes play a pivotal role in establishing a common comparison reference. The time unit selected to calculate the volume of water passing through the meter is guided by the type of water reticulation infrastructure within which the meter is installed. However, experience and literature show that a flow interval of less than 1 min would result in the application of unrealistic high flow rates. A simplified example for the determination of the weighted accuracy of a water meter monitoring a theoretical demand pattern illustrates the methodology used to establish the common comparison reference. Economic/financial analysis based on an income statement together with capital budgeting techniques assist with the determination of the financial suitability of investing in a new replacement water meter. This financial analysis includes various potential income and expenditure components that will result from the installation of a new water meter. Sensitivity analysis facilitates the decision-making process. The analysis of flow data by a computer program developed in context with the described methodology illustrates that the savings achieved by the improved accuracy of matching the optimally selected meter and a particular demand profile can finance the costs of such an investment. WaterSA Vol.27(4) 2001: 481-48

REDUCTION OF GIBBS PHENOMENON IN EOG SIGNAL MEASUREMENT USING THE MODIFIED DIGITAL STOCHASTIC MEASUREMENT METHOD

The method of digital stochastic measurement is based on stochastic analog-to-digital conversion, with a low-resolution A/D converters and accumulation. This method has been mainly tested and used for the measurement of stationary signals. This paper presents, analyses and discusses a simulation model development for an example of electrooculography (EOG) signal measurement in the time domain. Tests were carried out without adding a noise, and with adding a noise with various level of signal-to-noise ratio. For these values of signal-to-noise ratio, the mean and maximal relative errors are calculated and the significant influence of Gibbs phenomenon is noticed. In order to eliminate Gibbs phenomenon and decrease measurement error, a modified stochastic digital measurement method with overlapping measurement intervals has been developed and applied. On the basis of obtained results, the possibility of design and realization of an instrument with sufficient accuracy benefiting from the hardware simplicity of the method has been formulated. Also, the idea for the future research for developing a simulation model with a lower sampling frequency and implementing the proposed method is outlined

Optical technology Apollo extension system, phase A, volume 2. Section 3 - Experiments

Optical propagation in turbulent atmosphere, optical communication diagnostics, spaceborne heterodyne experiments, and ground support requirement

User data dissemination concepts for earth resources: Executive summary

The impact of the future capabilities of earth-resources data sensors (both satellite and airborne) and their requirements on the data dissemination network were investigated and optimum ways of configuring this network were determined. The scope of this study was limited to the continental U.S.A. (including Alaska) and to the 1985-1995 time period. Some of the conclusions and recommendations reached were: (1) Data from satellites in sun-synchronous polar orbits (700-920 km) will generate most of the earth-resources data in the specified time period. (2) Data from aircraft and shuttle sorties cannot be readily integrated in a data-dissemination network unless already preprocessed in a digitized form to a standard geometric coordinate system. (3) Data transmission between readout stations and central preprocessing facilities, and between processing facilities and user facilities are most economically performed by domestic communication satellites. (4) The effect of the following factors should be studied: cloud cover, expanded coverage, pricing strategies, multidiscipline missions