Disaggregation of net-metered advanced metering infrastructure data to estimate photovoltaic generation

2019 Fall.Includes bibliographical references.Advanced metering infrastructure (AMI) is a system of smart meters and data management systems that enables communication between a utility and a customer's premise, and can provide real time information about a solar array's production. Due to residential solar systems typically being configured behind-the-meter, utilities often have very little information about their energy generation. In these instances, net-metered AMI data does not provide clear insight into PV system performance. This work presents a methodology for modeling individual array and system-wide PV generation using only weather data, premise AMI data, and the approximate date of PV installation. Nearly 850 homes with installed solar in Fort Collins, Colorado, USA were modeled for up to 36 months. By matching comparable periods of time to factor out sources of variability in a building's electrical load, algorithms are used to estimate the building's consumption, allowing the previously invisible solar generation to be calculated. These modeled outputs are then compared to previously developed white-box physical models. Using this new AMI method, individual premises can be modeled to agreement with physical models within ±20%. When modeling portfolio-wide aggregation, the AMI method operates most effectively in summer months when solar generation is highest. Over 75% of all days within three years modeled are estimated to within ±20% with established methods. Advantages of the AMI model with regard to snow coverage, shading, and difficult to model factors are discussed, and next-day PV prediction using forecasted weather data is also explored. This work provides a foundation for disaggregating solar generation from AMI data, without knowing specific physical parameters of the array or using known generation for computational training

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

A hybrid-unit energy input-output model to evaluate embodied energy and life cycle emissions for China's economy

We develop a hybrid-unit energy input-output (I/O) model with a disaggregated electricity sector for China. The model replaces primary energy rows in monetary value, namely, coal, gas, crude oil, and renewable energy, with physical flow units in order to overcome errors associated with the proportionality assumption in environmental I/O analysis models. Model development and data use are explained and compared with other approaches in the field of environmental life cycle assessment. The model is applied to evaluate the primary energy embodied in economic output to meet Chinese final consumption for the year 2007. Direct and indirect carbon dioxide emissions intensities are determined. We find that different final demand categories pose distinctive requirements on the primary energy mix. Also, a considerable amount of energy is embodied in the supply chain of secondary industries. Embodied energy and emissions are crucial to consider for policy development in China based on consumption, rather than production. Consumption-based policies will likely play a more important role in China when per capita income levels have reached those of western countries

Scalable Data-driven Modeling and Analytics for Smart Buildings

Buildings account for over 40% of the energy and 75% of the electricity usage. Thus, by reducing our energy footprint in buildings, we can improve our overall energysustainability. Further, the proliferation of networked sensors and IoT devices in recent years have enabled monitoring of buildings to provide data at various granularity. For example, smart plugs monitor appliance level usage inside the house, while solar meters monitor residential rooftop solar installations. Furthermore, smart meters record energy usage at a grid-scale. In this thesis, I argue that data-driven modeling applied to the IoT data from a smart building, at varying granularity, in association with third party data can help to understand and reduce human energy consumption. I present four data-driven modeling approaches — that use sophisticated techniques from Machine Learning, Optimization, and Time Series Analysis — applied at different granularities. First, I study IoT devices inside the house and discuss an approach called NIMD that au- tomatically models individual electrical loads found in a household. The analytical model resulting from this approach can be used in several applications. For example, these models can improve the performance of NILM algorithms to disaggregate loads in a given household. Further, faulty or energy-inefficient appliances can be identified by observing deviations in model parameters over its lifetime. Second, I examine data from solar meters and present a machine learning framework called SolarCast to forecast energy generation from residential rooftop installations. The predictions enable exploiting the benefits of locally-generated solar energy. Third, I employ a sensorless approach utilizing a graphical model representation to re- port city-scale photovoltaic panel health and identify anomalies in solar energy production. Immediate identification of faults maximizes the solar investment by aiding in optimal operational performance. Finally, I focus on grid-level smart meter data and use correlations between energy usage and external weather to derive probabilistic estimates of energy, which is leveraged to identify the least efficient buildings from a large population along with the underlying cause of energy inefficiency. The identified homes can be targeted for custom energy efficiency programs

Assessing the thermal performance of phase change materials in composite hot humid/hot dry climates: an examination of office buildings in Abuja-Nigeria

PhD ThesisThe aim of this study is to investigate the possibility of using Phase Change Materials (PCM) in improving indoor thermal comfort while conserving electricity in office buildings in the composite Hot Humid/Hot Dry climate of Abuja, Nigeria. The first stage is a quantitative investigation of electricity consumption in 15 Nigerian office Buildings. Purpose-built mechanically cooled office buildings are selectively chosen across major Nigerian cities and climates. The surveyed data is analysed and used to construct a hypothetical office building as a base case. Scientifically validated software DesignBuilder v3 and EnergyPlus V6 and V7 are used for the parametric analysis of simulation results. The building simulations are used in two stages, firstly to test passive and climatically responsive scenarios to reduce electricity consumption then secondly to study the potential benefit of incorporating PCM in the building fabric and its effect on thermal comfort and electricity conservation. Results show that cooling, lighting, and appliance loads account for approximately 40%, 12% and 48% respectively of electricity consumption in the buildings audited. Power outages are frequently experienced necessitating alternative power usage. A data collection method is presented for energy auditors in locations where alternative back-up power is essential. Simulation results indicate that the magnitude of energy saving can be achieved by optimizing the passive and climate sensitive design aspects of the building and an electricity saving of 26% is predicted. Analysis indicates that it is difficult to achieve thermal comfort in office buildings in Abuja without mechanical cooling. Adding such a PCM to the building fabric of a cyclically cooled mechanical building may alleviate indoor discomfort for about 2 hours in case of power outage and is predicted to save 7% of cooling load. Cyclic cooling is the cooling of the interiors long enough to maintain comfort for a maximum duration within the working hours. The use of lightweight partitions instead of the heavyweight ones common in Nigeria is shown to a 2-fold improvement in consumption. Adding a PCM to light-weight partition walls with transition temperature of 24°C, conductivity of 0.5W/m K, and a thickness of 10mm gives the best predicted energy savings.Petroleum Technology Development Fund (PTDF), Nigeria

Satellite Gravimetry Applied to Drought Monitoring

11.1 Introduction...261 11.2 Satellite Gravimetry...262 11.3 Gravity Recovery and Climate Experiment...263 11.4 Hydrological Science Enabled by GRACE...264 11.5 Unique and Challenging Aspects of GRACE Data...265 11.6 Disaggregating and Downscaling GRACE Data...266 11.7 Drought Monitoring with GRACE...268 11.8 Future Prospects... 272 Acknowledgments....274 References.... 27

Cost gain of implementing load shifting in residential buildings

There is a clear trend today that we use more and more appliances with a higher power demand, something that is a real challenge for the electricity grid. Also, the shift towards electrification of the transport system gives a high volatility in consumption of electricity which is reflected in the distribution grid. Traditionally, the power distribution companies reinvest in grid upgrades to handle the increase in peak load demand. However, another alternative (short/midterm) solution to this problem is creating incentives to the customers to change their consumption patterns. This thesis investigates whether it is possible to control the use of household devices in order to reduce the electricity costs, and whether this process is economically feasible for the end users. To achieve this goal, a series of different grid tariff models were tested against real consumption patterns of buildings of different types. The results show that “Observed power” and “Subscribed power” tariff models compare to other studied models induce higher financial incentive to end-users to change their consumption behavior. In addition, the use of storage units and local solar production is another alternative to further increase the flexibility in Norwegian households.submittedVersionM-Ø