2004아테네 올림픽대회출전 한국레슬링 대표선수의 무산소성 운동능력 및 등속성 근력

This paper describes a new approach to online forecasting of power production from PV systems. The method is suited to online forecasting in many applications and in this paper it is used to predict hourly values of solar power for horizons of up to 36 h. The data used is 15-min observations of solar power from 21 PV systems located on rooftops in a small village in Denmark. The suggested method is a two-stage method where first a statistical normalization of the solar power is obtained using a clear sky model. The clear sky model is found using statistical smoothing techniques. Then forecasts of the normalized solar power are calculated using adaptive linear time series models. Both autoregressive (AR) and AR with exogenous input (ARX) models are evaluated, where the latter takes numerical weather predictions (NWPs) as input. The results indicate that for forecasts up to 2 h ahead the most important input is the available observations of solar power, while for longer horizons NWPs are the most important input. A root mean square error improvement of around 35% is achieved by the ARX model compared to a proposed reference model.

/pdf/online-short-term-solar-power-forecasting-1ljgpbshkb.pdf

Online Short-term Solar Power Forecasting

Buildings are among the largest consumers of electricity in the US. A significant portion of this energy use in buildings can be attributed to HVAC systems used to maintain comfort for occupants. In most cases these building HVAC systems run on fixed schedules and do not employ any fine grained control based on detailed occupancy information. In this paper we present the design and implementation of a presence sensor platform that can be used for accurate occupancy detection at the level of individual offices. Our presence sensor is low-cost, wireless, and incrementally deployable within existing buildings. Using a pilot deployment of our system across ten offices over a two week period we identify significant opportunities for energy savings due to periods of vacancy. Our energy measurements show that our presence node has an estimated battery lifetime of over five years, while detecting occupancy accurately. Furthermore, using a building simulation framework and the occupancy information from our testbed, we show potential energy savings from 10% to 15% using our system.

[ACM Press the 2nd ACM Workshop - Zurich, Switzerland (2010.11.02-2010.11.02)] Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building - BuildSys \'10 - Occupancy-driven energy management for smart building automation

Climate change is one of the greatest challenges facing humanity, and we, as machine learning experts, may wonder how we can help. Here we describe how machine learning can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by machine learning, in collaboration with other fields. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the machine learning community to join the global effort against climate change.

Tackling Climate Change with Machine Learning

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Modern Operating Systems

Rooftop solar deployments are an excellent source for generating clean energy. As a result, their popularity among homeowners has grown significantly over the years. Unfortunately, estimating the solar potential of a roof requires homeowners to consult solar consultants, who manually evaluate the site. Recently there have been efforts to automatically estimate the solar potential for any roof within a city. However, current methods work only for places where LIDAR data is available, thereby limiting their reach to just a few places in the world. In this paper, we propose DeepRoof, a data-driven approach that uses widely available satellite images to assess the solar potential of a roof. Using satellite images, DeepRoof determines the roof's geometry and leverages publicly available real-estate and solar irradiance data to provide a pixel-level estimate of the solar potential for each planar roof segment. Such estimates can be used to identify ideal locations on the roof for installing solar panels. Further, we evaluate our approach on an annotated roof dataset, validate the results with solar experts and compare it to a LIDAR-based approach. Our results show that DeepRoof can accurately extract the roof geometry such as the planar roof segments and their orientation, achieving a true positive rate of 91.1% in identifying roofs and a low mean orientation error of 9.3 degree. We also show that DeepRoof's median estimate of the available solar installation area is within 11% of a LIDAR-based approach.

https://dl.acm.org/doi/pdf/10.1145/3292500.3330741

DeepRoof: A Data-driven Approach For Solar Potential Estimation Using Rooftop Imagery

Homeowners are increasingly deploying grid-tied solar systems due to the rapid decline in solar module prices. The energy produced by these solar-powered homes is monitored by utilities and third parties using networked energy meters, which record and transmit energy data at fine-grained intervals. Such energy data is considered anonymous if it is not associated with identifying account information, e.g., a name and address. Thus, energy data from these "anonymous" homes is often not handled securely: it is routinely transmitted over the Internet in plaintext, stored unencrypted in the cloud, shared with third-party energy analytics companies, and even made publicly available over the Internet. Extensive prior work has shown that energy consumption data is vulnerable to multiple attacks, which analyze it to reveal a range of sensitive private information about occupant activities. However, these attacks are useless without knowledge of a home's location. Our key insight is that solar energy data is not anonymous: since every location on Earth has a unique solar signature, it embeds detailed location information. To explore the severity and extent of this privacy threat, we design SunSpot to localize "anonymous" solar-powered homes using their solar energy data. We evaluate SunSpot on publicly-available energy data from 14 homes with rooftop solar. We find that SunSpot is able to localize a solar-powered home to a small region of interest that is near the smallest possible area given the energy data resolution, e.g., within a ~500m and ~28km radius for per-second and per-minute resolution, respectively. SunSpot then identifies solar-powered homes within this region using crowd-sourced image processing of satellite data before applying additional filters to identify a specific home.

https://dl.acm.org/doi/pdf/10.1145/2993422.2993573

SunSpot: Exposing the Location of Anonymous Solar-powered Homes

Electric vehicles (EV) are growing in popularity as a credible alternative to gas-powered vehicles. These vehicles require their batteries to be “fueled up” for operation. While EV charging has traditionally been grid-based, use of solar powered chargers has emerged as an interesting opportunity. These chargers provide clean electricity to electric-powered cars that are themselves pollution free resulting in positive environmental effects. In this paper, we design a solar-powered EV charging station in a parking lot of a car-share service. In such a car-share service rental pick up and drop off times are known. We formulate a Linear Programming approach to charge EVs that maximize the utilization of solar energy while maintaining similar battery levels for all cars. We evaluate the performance of our algorithm on a real-world and synthetically derived datasets to show that it fairly distributes the available electric charge among candidate EVs across seasons with variable demand profiles. Further, we reduce the disparity in the battery charge levels by 60% compared to best effort charging policy. Moreover, we show that 80th percentile of EVs have at least 75% battery level at the end of their charging session. Finally, we demonstrate the feasibility of our charging station and show that a solar installation proportional to the size of a parking lot adequately apportions available solar energy generated to the EVs serviced.

Shared solar-powered EV charging stations: Feasibility and benefits

Understanding the energy usage of buildings is crucial for policy-making, energy planning, and achieving sustainable development. Unfortunately, instrumenting buildings to collect energy usage data is difficult and all publicly available datasets typically include only a few hundred homes within a region. Due to their relatively small size, these datasets provide limited insight and are insufficient for analyses that require a larger representation, such as an entire city or town. In recent years, utility companies have installed advanced electric and gas meters, i.e., "smart meters" that enable energy data collection on a massive scale. In this paper, we analyze such a dataset from a utility company that includes energy data from 14,836 smart meters covering a small city. We conduct a wide-ranging analysis of the city's gas and electric data to gain insights into the energy consumption of both individual homes and the city as a whole. In doing so, we demonstrate how city-scale smart meter datasets can answer a variety of questions on building energy consumption, such as the impact of weather on energy usage, the correlation between the size and age of a building and its energy usage, the impact of increasing levels of renewable penetration, etc. For example, we show that extreme weather events significantly increase energy usage, e.g., by 36% and 11.5% on hot summer and cold winter days, respectively. As another example, we observe that 700 homes are highly energy inefficient as its energy demand variability is twice that of the aggregate grid demand. Finally, we study the impact of increasing level of renewable integration in homes and show that solar penetration rates higher than 20% of demand increases the risk of over-generation and may impact utility operations.

https://dl.acm.org/doi/pdf/10.1145/2993422.2993425

Analyzing Energy Usage on a City-scale using Utility Smart Meters

The proliferation of solar deployments has significantly increased over the years. Analyzing these deployments can lead to the timely detection of anomalies in power generation, which can maximize the benefits from solar energy. In this paper, we propose SolarClique, a data-driven approach that can flag anomalies in power generation with high accuracy. Unlike prior approaches, our work neither depends on expensive instrumentation nor does it require external inputs such as weather data. Rather our approach exploits correlations in solar power generation from geographically nearby sites to predict the expected output of a site and flag anomalies. We evaluate our approach on 88 solar installations located in Austin, Texas. We show that our algorithm can even work with data from few geographically nearby sites (>5 sites) to produce results with high accuracy. Thus, our approach can scale to sparsely populated regions, where there are few solar installations. Further, among the 88 installations, our approach reported 76 sites with anomalies in power generation. Moreover, our approach is robust enough to distinguish between reduction in power output due to anomalies and other factors such as cloudy conditions.

https://dl.acm.org/doi/pdf/10.1145/3209811.3209860

Srinivasan Iyengar

Papers

DeepRoof: A Data-driven Approach For Solar Potential Estimation Using Rooftop Imagery

SunSpot: Exposing the Location of Anonymous Solar-powered Homes

Shared solar-powered EV charging stations: Feasibility and benefits

Analyzing Energy Usage on a City-scale using Utility Smart Meters

SolarClique: Detecting Anomalies in Residential Solar Arrays