Localized solar power prediction based on weather data from local history and global forecasts

With the recent interest in net-zero sustainability for commercial buildings, integration of photovoltaic (PV) assets becomes even more important. This integration remains a challenge due to high solar variability and uncertainty in the prediction of PV output. Most existing methods predict PV output using either local power/weather history or global weather forecasts, thereby ignoring either the impending global phenomena or the relevant local characteristics, respectively. This work proposes to leverage weather data from both local weather history and global forecasts based on time series modeling with exogenous inputs. The proposed model results in eighteen hour ahead forecasts with a mean accuracy of $\approx$ 80\% and uses data from the National Ocean and Atmospheric Administration's (NOAA) High-Resolution Rapid Refresh (HRRR) model.Comment: 5 pages, 4 figures, 3 tables, 45th IEEE Photovoltaic Specialists Conference (PVSC

Occupant Plugload Management for Demand Response in Commercial Buildings: Field Experimentation and Statistical Characterization

Commercial buildings account for approximately 36% of US electricity consumption, of which nearly two-thirds is met by fossil fuels [1] resulting in an adverse impact on the environment. Reducing this impact requires improving energy efficiency and lowering energy consumption. Most existing studies focus on designing methods to regulate and reduce HVAC and lighting energy consumption. However, few studies have focused on the control of occupant plugload energy consumption. In this study, we conducted multiple experiments to analyze changes in occupant plugload energy consumption due to monetary incentives and/or feedback. The experiments were performed in government office and university buildings at NASA Research Park located in Moffett Field, CA. Analysis of the data reveal significant plugload energy reduction can be achieved via feedback and/or incentive mechanisms. Autoregressive models are used to predict expected plugload savings in the presence of exogenous variables. The results of this study suggest that occupant-in-the-loop control architectures have the potential to reduce energy consumption and hence lower the carbon footprint of commercial buildings.Comment: 20 pages, 15 figures, 4 tables, preprin

Initial Evaluations of LoC Prediction Algorithms Using the NASA Vertical Motion Simulator

Flying near the edge of the safe operating envelope is an inherently unsafe proposition. Edge of the envelope here implies that small changes or disturbances in system state or system dynamics can take the system out of the safe envelope in a short time and could result in loss-of-control events. This study evaluated approaches to predicting loss-of-control safety margins as the aircraft gets closer to the edge of the safe operating envelope. The goal of the approach is to provide the pilot aural, visual, and tactile cues focused on maintaining the pilot's control action within predicted loss-of-control boundaries. Our predictive architecture combines quantitative loss-of-control boundaries, an adaptive prediction method to estimate in real-time Markov model parameters and associated stability margins, and a real-time data-based predictive control margins estimation algorithm. The combined architecture is applied to a nonlinear transport class aircraft. Evaluations of various feedback cues using both test and commercial pilots in the NASA Ames Vertical Motion-base Simulator (VMS) were conducted in the summer of 2013. The paper presents results of this evaluation focused on effectiveness of these approaches and the cues in preventing the pilots from entering a loss-of-control event

A Prediction and Decision Framework for Energy Management in Smart Buildings

<p>By 2040, global CO2 emissions and energy consumption are expected to increase by 40%. In the US, buildings account for 40% of national CO2 emissions and energy consumption, of which 75% is met by fossil fuels. Reducing this impact on the environment requires both improved building energy efficiency and increased renewable utilization. To this end, this dissertation presents a demand-supplystorage- based decision framework to enable strategic energy management in smart buildings. This framework includes important but largely unaddressed aspects pertaining to building demand and supply such as occupant plugloads and the integration of weather forecast-based solar prediction, respectively. We devote the first part of our work to study occupant plugloads, which account for up to 50% of demand in high performance buildings. We investigate the impact of plugload control mechanisms based on the analysis of real-world data from experiments we conducted at NASA Ames sustainability base and Carnegie Mellon University (SV campus). Our main contribution is in extending existing demand response approaches to an occupant-in-the-loop paradigm. In the second part of this work, we describe methods to develop weather forecastbased solar prediction models using both local sensor measurements and global weather forecast data from the National Ocean and Atmospheric Administration (NOAA).We contribute to the state-of-the-art solar prediction models by proposing the incorporation of both local and global weather characteristics into their predictions. This weather forecast-based solar model plus the plugload-integrated demand model, along with an energy storage model constitutes the weather-driven plugloadintegrated decision-making framework for energy management. To demonstrate the utility of this framework, we apply it to solve an optimal decision problem with the objective of minimizing the energy-related operating costs associated with a smart building. The findings indicate that the optimal decisions can result in savings of up to 74% in the expected operational costs. This framework enables inclusive energy management in smart buildings by accounting for occupants-in-the-loop. Results are presented and discussed in the context of commercial office buildings.</p