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STUDENT DESIGN SHOWCASE

Renewable energy sources are an important and growing component of our global energy sys-tem. According to the REN21 (Renewable Energy Policy Network for the 21st Century) 2015 Global

Status Report, renewables accounted for nearly 23 percent of global electricity-generating capacity at the end of 2014 (www.ren21.net/wp-content/uploads/2015/07/REN12-GSR2015_Onlinebook_low1.pdf). Solar photovoltaic (PV) generation accounts for nearly 1 percent of global capac-ity and is growing rapidly. In 2014, 40 GW of global PV capacity was added, led by China, Japan, and the US. (Ger-many remains the leader in overall capacity.)

Business decisions about deploying PV arrays rely on historical solar irradiation data, also known as insolation, which measures the solar energy that reaches the earth’s surface. This usable energy varies according to latitude, elevation, season, and weather conditions. Figure 1 shows

the annualized average solar irra-diation for the US, using 1998–2009 data compiled by the National Re-newable Energy Laboratory (NREL; www.nrel.gov/gis/solar.html).

Although such data clearly shows the comparative potential for PV in di� erent regions—for example, New Mexico versus Michigan—it’s not helpful in managing the day-to-day or hour-to-hour energy needs

of an individual home or local power grid. First, the data is too coarse, typically compiled on a 10- or 40-km grid. Second, historical monthly or annual averages won’t tell you what’s going to happen today. A homeowner needs to know whether to expect enough solar power to run a load through the washing machine. An electric power utility needs to know whether it will receive power from the solar panels, or whether it needs to provide additional power to compensate for a lack of sunlight.

PROJECT SOLProject Sol proposes to solve this problem by deploying many inexpensive network-connected sensors. The solu-tion works by aggregating insolation data from each sensor into a cloud-based database. Subscribers to the data can use visualization and analytics to monitor and react to current conditions as well as predict future energy production.

Let the Sun ShineGreg Byrd, North Carolina State University

Clemson University students designed an

inexpensive sensor node and cloud computing

infrastructure to collect real-time, localized

solar irradiation data. This data can be used by

consumers and utilities to predict the availability

of solar-generated electricity and to manage

its use.

J U LY 2 0 1 6 95

EDITOR GREG BYRD North Carolina State University; gbyrd@computer.org

IBM—in partnership with Clemson University’s School of Computing and College of Business and Behavioral Science—sponsored Project Sol as part of Clemson’s Extreme Orange program Extreme Orange is a two-semester se-quence for juniors and seniors inter-ested in applying programming and business skills to real-world problems. IBM contributes project ideas and tech-nical mentorship, and students work with university faculty mentors to explore and implement the core ideas. Business principles and entrepreneur-ialism are integral to the process, and each project team includes students from both business and computer sci-ence or engineering disciplines.

SYSTEM DESIGNFigure 2 shows Project Sol’s overall system design. At the bottom are the sensors, which use a photoresistor to detect the amount of light. Each sensor transmits its data to a message broker using the Message Queuing Telemetry Transport (MQTT) protocol. The bro-ker collects the data and inserts it into a distributed database. The current Project Sol system presents the data to a user through a browser-based dash-board, developed with the Shiny Web application framework from RStudio. In the future, analytics services will also use the data for more sophisti-cated applications, connecting pre-dicted energy output with weather forecasts, consumer behavior, and utility pricing models.

SENSOR NODEThe sensor node must be inexpensive, reliable, and durable. The cost must be low because the best use case is to de-ploy lots of them. Because the sensor nodes are cheap, they should also last a long time—it wouldn’t be cost-e� ective to service or replace them frequently. The sensor node must be connected to a network to relay information to the

message broker, using the MQTT pro-tocol (described in the next section). For the prototype, the team selected a Raspberry Pi B+ computer, running the Raspbian OS. (Raspbian is a port of Debian Linux, optimized for Rasp-berry Pi hardware.) The network con-nection is Wi-Fi, but that could easily be replaced with cellular or Zigbee (based on IEEE 802.15.4), depending on the deployment.

Attached to the CPU is a simple resistance-capacitance circuit con-sisting of a capacitor in series with a photo resistor. The CPU uses a simple loop and an analog-to-digital con-verter to time how long the capacitor takes to charge. With more light, re-sistance decreases and charging time is faster. In the future, the team will investigate using an actual solar cell to directly measure energy output.

PROJECT DETAILS

» Title: Project Sol (IBM Extreme Orange)

» School: Clemson University, Clemson, South Carolina

» Students: Daniel Branum, Ben Coomes, Jon Francis, Matthew Furlong,

Mike Gavris

» Faculty mentors: Jim Martin, Jianwei Liu

Figure 1. Average solar irradiation across the US from 1998 to 2009 (www.nrel.gov/gis/solar.html).

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STUDENT DESIGN SHOWCASE

MESSAGE BROKER AND MQTTMQTT is a lightweight network proto-col that utilizes a publish/subscribe messaging pattern. It runs on top of TCP/IP, or any protocol that provides ordered, lossless packet delivery. An MQTT client publishes information that other clients might be interested in, and it might subscribe to (express interest in) certain types of data. This is known as a data-centric style of communication, because the data, not an address, determines who’ll re-ceive a packet. This communication pattern is especially useful for In-ternet of Things applications, where

addresses might not be known ahead of time.

The message broker implements the server side of the MQTT protocol, and provides a mechanism for clients to communicate with each other. It accepts subscription requests from multiple clients so that, when a data message arrives that matches a sub-scription, that message is forwarded to the subscribing clients. Each data message contains a topic name, a string that describes the type of data. This is matched against subscription requests (which can include wild-cards) to determine which clients should receive the message.

Mosquitto is used as the message broker for the prototype. Mosquitto is an Eclipse community open source project, with versions in C++ and Py-thon. On the client side, Project Sol uses the Python version of the Eclipse Paho MQTT client.

DATABASE AND WEB-BASED DASHBOARDSolar information collected from the sensors is formatted as JavaScript Object Notation files and stored in an IBM Cloudant database. Cloudant is a document-based NoSQL database, founded on the Apache CouchDB project. Its design allows scalability through the use of clustering and au-tomatic data rebalancing. IBM’s Blue-mix cloud application platform offers Cloudant as a service.

The students built a Web-based dashboard to visualize the data, as shown in Figure 3. A user can opt to see sensor locations on a map, select specific sensors, and select dates and times for data to view. The underlying data processing is specified using the R language, and the dashboard is built using Shiny. Shiny allows users, even those with no knowledge of HTML or other Web development tools, to apply R’s analysis and visualization capabili-ties to Web applications.

FUTURE DEVELOPMENT: PREDICTIVE ANALYTICSThe availability of near-real-time local-ized solar data enables powerful predic-tions about near-term solar generation capacity. PV generation is very sensitive to cloud cover and other environmen-tal factors, and changes happen very rapidly, compared with other power sources. Recent smart grid advances and distributed co-generation use so-phisticated control algorithms to re-spond to changes in capacity and load.

Platforms such as IBM Watson can apply analytics and machine learning to combine weather, solar- irradiation, and load-balance data, thereby allow-ing better predictive decisions for shifting between power sources,

SUBMIT YOUR PROJECT

We want to hear about interesting student-led design projects in computer science and engineering. If you’d like to see your project featured in this

column, complete the submission form at www.computer.org/student-showcase.

Analytics andprediction

Database(Cloudant)

MQTT broker(Mosquitto)

Sensors(Raspberry Pi)

Dashboard(R Shiny)

Figure 2. Project Sol system design. Sensors transmit data to a message broker, the broker then collects the data into a distributed database, and the user accesses the data through a browser-based dashboard. MQTT: Message Queuing Telemetry Transport.

J U LY 2 0 1 6 97

adjusting pricing policies, and so forth. Recent research has focused on predicting demand, but increased reli-ance on solar and wind power will re-quire better short-term predictions of generation capacity.

Project Sol focuses on the abil-ity to capture and analyze large amounts of solar irra-

diation data. Its success will rely on the deployment of a large number of

network-connected sensors. Solar in-stallation companies would clearly be interested, because such data could persuade consumers who are con-sidering an investment in PV panels. Utilities could also deploy the sensors as part of their smart-meter infrastruc-ture, or they could reward consumers who are willing to collect data. Con-sumers who already have solar in-stallations could use the technology to better monitor and manage their systems to get the best return on their

investment. It’ll be interesting to see whether the benefits will outweigh the costs of collecting, maintaining, and analyzing such data.

Figure 3. Dashboard display of solar sensor data. Users can select views based on dates, times, and sensor locations.

GREG BYRD is associate head of

the Department of Electrical and

Computer Engineering at North

Carolina State University. Contact

him at gbyrd@computer.org.

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