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Inverse Modeling Using a Wireless Sensor Network (WSN) for Personalized Daylight Harvesting

Metadata Updated: December 7, 2023

Smart lighting systems in low energy commercial buildings can be expensive to implement and commission. Studies have also shown that only 50% of these systems are used after installation, and those used are not operated at full capacity due to inadequate commissioning and lack of personalization. Wireless sensor networks (WSN) have great potential to enable personalized smart lighting systems for real-time model predictive control of integrated smart building systems. In this paper we present a framework for using a WSN to develop a real-time indoor lighting inverse model as a piecewise linear function of window and artificial light levels, discretized by sub-hourly sun angles. Applied on two days of daylight and ten days of artificial light data, this model was able to predict the light level at seven monitored workstations with accuracy sufficient for daylight harvesting and lighting control around fixed work surfaces. The reduced order model was also designed to be used for long term evaluation of energy and comfort performance of the predictive control algorithms. This paper describes a WSN experiment from an implementation at the Sustainability Base at NASA Ames, a living laboratory that offers opportunities to test and validate information-centric smart building control systems.

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Public: This dataset is intended for public access and use. License: No license information was provided. If this work was prepared by an officer or employee of the United States government as part of that person's official duties it is considered a U.S. Government Work.

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Dates

Metadata Created Date November 12, 2020
Metadata Updated Date December 7, 2023
Data Update Frequency irregular

Metadata Source

Harvested from NASA Data.json

Additional Metadata

Resource Type Dataset
Metadata Created Date November 12, 2020
Metadata Updated Date December 7, 2023
Publisher Dashlink
Maintainer
Identifier DASHLINK_867
Data First Published 2013-12-18
Data Last Modified 2020-01-29
Public Access Level public
Data Update Frequency irregular
Bureau Code 026:00
Metadata Context https://project-open-data.cio.gov/v1.1/schema/catalog.jsonld
Metadata Catalog ID https://data.nasa.gov/data.json
Schema Version https://project-open-data.cio.gov/v1.1/schema
Catalog Describedby https://project-open-data.cio.gov/v1.1/schema/catalog.json
Harvest Object Id 9866a85c-03ca-4191-97ac-4262ec2eb130
Harvest Source Id 58f92550-7a01-4f00-b1b2-8dc953bd598f
Harvest Source Title NASA Data.json
Homepage URL https://c3.nasa.gov/dashlink/resources/867/
Program Code 026:029
Source Datajson Identifier True
Source Hash daa983f27381b4fb66404831c9632b693818f415b047da4f7f72f17b418db369
Source Schema Version 1.1

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