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Castello, Charles C

Publications and source records attributed to Castello, Charles C.

Gamification and Visualization of Sensor Data Analysis in Research Buildings

The use of video game elements in non-gaming systems, or gamification , has potential value in transforming data analysis. Our study focused on creating a web-based videogame that models two physical test buildings, each of which contains hundreds of sensors. After the application renders the models, the player can walk through the environments and interact with the virtual representations of the sensors inside. Rather than trudging through a database with textual commands and screens full of data, the user can (virtually) walk up to a sensor and view its data graphically. But these features only scratch the surface of what is possible using our new gamification approach. We anticipate being able to show that recent progress in game design techniques and capacities can contribute to the field of analysis through gamification. The net result could be more stimulating, intuitive, user-friendly interfaces, as well as potentially more informative and insightful applications.

Stone, Jackson A↗

Sensor Data Management, Validation, Correction, and Provenance for Building Technologies

Oak Ridge National Laboratory (ORNL) conducts research on technologies that use a wide range of sensors to develop and characterize building energy performance. The management of high-resolution sensor data, analysis, and tracing lineage of such activities is challenging. Missing or corrupt data due to sensor failure, fouling, drifting, calibration error, or data logger failure is another issue. This paper focuses on sensor data management, validation, correction, and provenance to combat these issues, ensuring complete and accurate sensor datasets for building technologies applications and research. The design and development of two integrated software products are discussed: Sensor Data Validation and Correction (SensorDVC) and the Provenance Data Management System (ProvDMS) platform.

Data management↗

Control Strategies for Electric Vehicle (EV) Charging Using Renewables and Local Storage

The increase of electric vehicle (EV) and plug-in hybrid-electric vehicle (PHEV) adoption creates a need for more EV supply equipment (EVSE) infrastructure (i.e., EV chargers). The impact of EVSE installations could be significant due to limitations in the electric grid and potential demand charges for residential and commercial customers. The use of renewables (e.g., solar) and local storage (e.g., battery bank) can mitigate loads caused by EVSE on the electric grid. This would eliminate costly upgrades needed by utilities and decrease demand charges for consumers. This paper aims to explore control systems that mitigate the impact of EVSE on the electric grid using solar energy and battery banks. Three control systems are investigated and compared in this study. The first control system discharges the battery bank at a constant rate during specific times of the day based on historical data. The second discharges the battery bank based on the number of EVs charging (linear) and the amount of solar energy being generated. The third discharges the battery bank based on a sigmoid function (non-linear) in response to the number of EVs charging, and also takes into consideration the amount of renewables being generated. The first and second control systems recharge the battery bank at night when demand charges are lowest. The third recharges the battery bank at night and during times of the day when there is an excess of solar. Experiments are conducted using data from a private site that has 25 solar-assisted charging stations at Oak Ridge National Laboratory (ORNL) in Oak Ridge, TN and 4 at a public site in Nashville, TN. Results indicate the third control system having better performance, negating up to 71% of EVSE load, compared with the second control system (up to 61%) and the first control system (up to 58%).

Castello, Charles C↗