(Created page with "=='''Introduction'''== thumb|Fig 1: Control and Energized Glass solar modules at the Keweenaw Research Center The goal of this project was to tes...") |
Page script (talk | contribs) (Move Template:Page data to the bottom) |
||
| (27 intermediate revisions by 3 users not shown) | |||
| Line 1: | Line 1: | ||
== | == Energized Glass Report == | ||
'''EG Solar Team''' | |||
'''Team Members''': Madi Cleary, John Frischmon, Travis Durgan, Adam Kallioinen, Simon Eddy, Sean Smith, and Lacey Englebert | |||
'''Enterprise:''' Alternative Energy Enterprise | |||
'''Advisor:''' Jay Meldrum | |||
'''Date:''' May 4, 2018 | |||
Michigan Technological University | |||
Houghton, MI 49931 | |||
== 1. Introduction == | |||
[[File:EGandControl.PNG|thumb|Figure 1: Control group shown on the left and the EG on the right]] | |||
In the fall of 2017, the [http://web.archive.org/web/20200401162139/http://aee-mtu.org:80/teams/solar.html Alternative Energy Enterprise (AEE) EG Solar Team] formed to test the performance effects of [http://www.energizedglass.com/ Energized Glass] (EG) on solar panels throughout the harsh winters of Houghton County, Michigan. With an array of twelve Yingli 240 (yl204p-29b) solar panels - six equipped with EG and six maintaining the original factory panel build, the use of EG is to be tested as a means of melting panel surface snow buildup while keeping from using enough energy to outweigh any benefits from having a clean panel surface. The array of test panels is mounted and being monitored at the [http://www.mtukrc.org/ Keweenaw Research Center] (KRC) in Calumet, Michigan shown in figure 1. Power usage and production from the panels utilizing EG will be compared with the production data from the stock panels throughout the winter to gain insight into any benefits or drawbacks to using EG. | |||
== 2. Background == | |||
The goal of the experiments with EG is to see how effective it can be at removing snow buildup while maintaining sufficient energy production. The Energized Glass company is based in Fort Collins, Colorado. Low-E glass was originally developed for use in homes and offices to improve comfort, cut down on heat loss, and reduce condensation on windows during the winter season. It is a clear low emissivity (low-e) film that reflects some of the interior generated radiant energy back into the structure. The EG system magnifies the comfort and condensation control benefits by applying controlled amounts of electricity to the metal low-e film. This causes the temperature of the glass to rise [1]. This rise in temperature is what will be used to clear the test panels of any snow cover. The glass can be set to activate within the desired temperature range and the temperature of the glass is monitored through thermistors connected to the center of the pane. The EG covered panels provided to the team draw about 5A of current when the glass is active. Data collected over the course of the winter will tell if the power used by the glass counteracts the benefits of faster snow removal on the panels. | |||
== 3. Results == | |||
'''3.1 Temperature Sensor Testing''' | |||
This experiment was performed in the fall of 2017. The EG system relies on one temperature sensor located in the center of the glass pane. The team aimed to confirm that the panel’s surface temperature was constant throughout in order determine if addition equipment was necessary and if the singular sensor could be moved to a more secure location. To ensure accurate temperature readings, a series of tests were conducted to compare glass surface temperatures at various locations. An Arduino temperature sensor was utilized for this experiment. After calibration, the EG was set to two different temperatures: 90℉ and 96℉. The surface temperature was recorded every five seconds for a two minute period at nine separate locations. | |||
Once collected, the data was statistically analyzed to ensure temperature uniformity. To start, outlying data points were removed. Next, the 95% confidence interval was found for each location. If two confidence intervals overlap, they are considered to be statically the same. | |||
Since there was a broad range of results, temperature locations were split into Top-Middle-Bottom and Left-Middle-Right to narrow the analysis. After repeating calculations, conclusions could be made about each zone. The top and middle sectors were the same but differed from the bottom. The left, middle, and right divisions are statistically different but nominally similar. | |||
These statistical patterns are comparable to the 96℉ data set. Although both data sets showed variance between panel zones, it was only by about a degree or so. This narrow variance in temperature was found to be sufficient for the scope of this project. From this, it was determined that the panel temperature is fairly constant throughout, no additional temperature equipment was necessary for further experimentation, and the current sensor could be moved to the outer edge of each panel. | |||
'''3.2 Snow Melt Testing''' | |||
To determine the viability of Energized Glass on solar panels for snow removal, multiple experiments with different objectives were conducted. This allowed for a plethora of data to better understand the effectiveness of the EG. Specifically, two arrays were used in all experiments, one with the EG and one without, to properly compare the impact EG has on snow removal. | |||
Before any snow melt experiments could be done, a few systems needed to be put in place to be able to turn the EG panels on/off, adjust EG temperature settings, and record the power production from both arrays. To turn the EG panels on or off, the team decided to use the Minuteman system. This simple system allowed the team to use a VPN to connect to the KRC’s network remotely to control the power supplied to the EG. To adjust the EG panel temperatures, the team used the existing MiWi network that has been previously implemented. Power production data was tracked and stored for all experiments using a solar monitoring program known as Enphase. Enphase tracks and stores power production in five minute increments throughout the day for each individual panel within the control and EG arrays. With module control and data collection systems in place, the team could then continue forward with planned experiments. | |||
'''Experiment 1''' | |||
The goal of the first experiment in 2018 was to determine what kind of impacts the EG has on the solar panels’ power production. With this information, the team was able to determine if the EG would cause significant losses year round. To conduct this test, both sets of panels were cleared of any snow. | |||
The power production values for each panel were manually entered into a spreadsheet in five minute increments where they could be easily analyzed. Power production was summed for each array to get total values for a given day. | |||
After conducting an Analysis of Variance (ANOVA) and t-test, it was concluded that the two sets were statistically different. Next, the data was averaged for each five minute increment to accurately graph power production from each array over the course of the entire day. From this, the team concluded that the EG caused a 30% reduction in power production. | |||
The 30% loss in power production can be attributed to in part, the type of glass used for the EG system. Typical solar panel glass has small ridges and prisms built into it. Each time light hits a glass surface, part of it is absorbed and refracted while the rest is reflected. By increasing the number of surfaces, solar panels are able to take in more light photons and produce more power. The EG system used the Pilkington Energy Advantage glass. As outlined in the product brochure, this glass is flat and transmits 73 to 75% of the solar energy that hits it [2]. This translates to a 25 to 27% reflection that does not reach the solar panel glass [2]. It was concluded that this type of glass caused the initial EG disadvantage. This was considered a tremendous discovery as it accurately explained why the EG panels were consistently producing less power than the control panels under identical conditions. | |||
'''Experiment 2''' | |||
The second overarching experiment of this semester was to determine the overall efficiency of the EG when all EG panels were powered on. This was completed twice, both times with about a half of an inch of snow coverage. | |||
One goal of this experiment was to accurately compare the net power production of the EG panels to the power production of the control panels. Net power production is the power produced over the course of the day combined with the power required to heat the EG panels. Simply put, net power is the power produced minus the power required. The data taken from Enphase does not take the power required to heat the EG panels into consideration and therefore it had to be included in the calculations by the team. To calculate the power required to heat the EG panels, a software called MiWi was used to track energy consumption. All the panels were turned on, and the energy required to heat the EG panels was recorded and averaged across all panels. The result was that each panel would require 0.3409 kWh over the course of one hour, this was calculated to be the same as 28.4 W for five minutes. Unit conversions from kilowatts to watts and one hour to five minutes are as follows: | |||
(0.3409 kWh / 1 h) * (1000 W / kW) * (5 / 60 minutes) = 28.4 W for every five minutes | |||
Power requirements were determined for five minutes since that is the same interval used by Enphase. By doing this, the team was able to simplify net power calculations. | |||
After measuring the power required by the EG, the team was able to determine the net power produced by the EG panels for each trial and carried through similar calculations to that of Experiment 1. | |||
Although both tests showed a general trend of lowered efficiency, they followed different patterns to reach those final numbers. On February 15th the EG took a considerable amount of power to initially melt the snow, and the control power output was relatively low and stagnant. However on March 8th the EG required less power to melt the snow, and, unlike the results from February 15th, the control panels were able to naturally melt the snow shortly after the EG was cleared. | |||
After reviewing the weather data and time-lapse photos provided by the KRC, the team determined two factors that most likely caused these differences between tests. The first factor is the outside air temperature before and during the experiment. On February 15th, the air temperature was greater than 32°F, so any snow on the panels would’ve had a relatively high water content and be a “wetter” snow. The air temperature then dropped below 32°F thus causing the wet snow to freeze into an icy layer before the EG was turned on. In comparison, the air temperature on the 8th remained well below 32°F, meaning the snow was drier and easier to melt. From this, one could conclude that the snow was more icy and harder to melt off on the 15th than on the 8th. | |||
The second factor that most likely lead to the control panels being able to naturally melt off on March 8th but not February 15th was the amount of relative sunlight. By using the time-lapse photos provided by the KRC, the 15th was relatively cloudy and foggy while the 8th was very sunny. Over the course of the semester, direct sunlight has been shown to melt snow off of the panels quicker than diffused light. | |||
After conducting these tests, the team realized that for both trials the EG was turned on longer than absolutely necessary and that power production could be optimized. To optimize power production, the team determined when the EG effectively removed enough snow using the timelapse photos provided by the KRC. Assuming that the EG was turned off immediately after the snow melted, the group applied the net power calculations only during the snow removal process. This optimized the net power production of the EG throughout the course of each day. With the EG power production optimized, the group was able to recalculate the potential efficiency of each test. | |||
When optimized, the EG efficiencies alternated, and Test #1 on the 15th had a better performance than Test #2 on the 8th. As discussed earlier, this is possibly due to the different types of snow and sunlight. It is also important to note that the control panels remained completely covered for the entire day on February 15th and slowly melted off naturally on March 8th. | |||
Over the course of the semester, the team noticed that snow tends to stick to the EG panels when they were not running. One final test was conducted to quantify this phenomena. The test consisted of allowing equal amounts of snow to fall onto the panels and letting them to melt naturally, without the assistance of the EG, over the course of a clear and sunny day. Both arrays were subjected to the same amount of snowfall and identical weather conditions with the only variable being the type of outermost glass. Without the support from the heated EG, the EG array took over ten times longer to melt snow than the control panel. It was concluded that the EG caused a 83% reduction over the course of the day, which includes the known 30% reduction. | |||
== 4. Conclusions == | |||
From the experiments conducted this semester, the group was able to make a couple conclusions regarding the implementation of Energized Glass. The first conclusion is that the EG itself causes a 30% reduction in standard power generation. It was determined that this is due to the type of glass that the EG was installed on. Secondly, snow tends to stick to the EG when it is not heated. No further research was conducted to find the causes. | |||
The team was not able to make a statistically valid conclusion when it came to the snow melt efficiency of the EG with the data collected during the semester. Initial data suggested that EG is not a viable option; however, after optimization, the possibility increased. Even though the efficiency increased when the EG was optimized, final results conflicted. One test showed that EG produced more power than the control panels while another showed less. | |||
Few tests were conducted due to a number of limitations. An overarching limit was weather once the system was assembled. The number of opportunities to run an experiment was restricted by the amount and time of snowfall. In general this winter, snowfall would taper off towards the middle of the night. This proved to be an issue since the team could not measure power production at night time. In addition, not enough snow would accumulate on the panels to run a test. When producing power during the day, the panels were naturally warm. This warmth prevented snow from collecting on top of the control panels. Finally, the cameras that were used to monitor the panels would become covered with snow and ice up. This made it difficult to determine when to turn the EG on and off and to conduct tests in general. | |||
== 5. Recommendations for Future Work == | |||
A common phrase in statistical analysis is “more data means more answers.” In order to have conclusive results, the team would need to conduct more experiments. The first type would be basic “all on” EG tests. This is what the team has done the entire semester. Once enough tests are completed and general trends can be identified, the team could move onto different tests. New experiments would include only turning the bottom row of EG panels on and setting the EG to various temperatures. | |||
On top of more experiments, the group could try different analysis methods to better compare the the two arrays. For example, the power production differences could have been calculated using the percent of rated power. This is common within the solar industry. | |||
In addition, more research regarding panel losses could be done. It was identified that the glass in the EG caused a power reduction, so the implementation of a different glass could increase the overall performance. Further research can also be done to determine if a second layer of glass is needed at all and if the EG system can be applied to a single layer of solar panel glass. | |||
In general, the EG system needs to be put into a centralized system for realistic and practical purposes. Currently three systems are running to use and analyze the EG panels. One system turns the panels on (MinuteMan), one system changes EG temperature settings (MiWi), and one system records power production without considering power required to heat the panels (Enphase). The team had to learn how to use all systems, and the information from these systems then had to be manually entered into a spreadsheet and analyzed. Realistically, a consumer does not have the time to carry out such a process. The need for a centralized program that can turn on the EG, change the EG temperature setting, and record EG power production that considers the power required to heat the EG panels is greatly needed. | |||
Lastly, one huge drawback the team experienced throughout the winter was the limited range of the MiWi system. The KRC is located approximately 50 feet from the EG panels and the MiWi system would not work at this range. The only way the team could use the MiWi was by standing within five feet of the EG panels with a wireless laptop, which is not practical in the winter months. | |||
== 7. References == | |||
[1] Lundahl, D. (n.d.). Welcome to Energized Glass - Perfecting Radiant Energy. Retrieved December 12, 2017, from http://www.energizedglass.com/ | |||
[2]Pilkington. Pilkington Energy Advantage. Pilkington Energy Advantage, March 2012, http://assetmanager-ws.pilkington.com/fileserver.aspx?cmd=get_file&ref=USA175&cd=cd | |||
{{Page data}} | |||
Latest revision as of 20:03, 2 March 2022
Energized Glass Report[edit | edit source]
EG Solar Team Team Members: Madi Cleary, John Frischmon, Travis Durgan, Adam Kallioinen, Simon Eddy, Sean Smith, and Lacey Englebert
Enterprise: Alternative Energy Enterprise
Advisor: Jay Meldrum
Date: May 4, 2018
Michigan Technological University
Houghton, MI 49931
1. Introduction[edit | edit source]
In the fall of 2017, the Alternative Energy Enterprise (AEE) EG Solar Team formed to test the performance effects of Energized Glass (EG) on solar panels throughout the harsh winters of Houghton County, Michigan. With an array of twelve Yingli 240 (yl204p-29b) solar panels - six equipped with EG and six maintaining the original factory panel build, the use of EG is to be tested as a means of melting panel surface snow buildup while keeping from using enough energy to outweigh any benefits from having a clean panel surface. The array of test panels is mounted and being monitored at the Keweenaw Research Center (KRC) in Calumet, Michigan shown in figure 1. Power usage and production from the panels utilizing EG will be compared with the production data from the stock panels throughout the winter to gain insight into any benefits or drawbacks to using EG.
2. Background[edit | edit source]
The goal of the experiments with EG is to see how effective it can be at removing snow buildup while maintaining sufficient energy production. The Energized Glass company is based in Fort Collins, Colorado. Low-E glass was originally developed for use in homes and offices to improve comfort, cut down on heat loss, and reduce condensation on windows during the winter season. It is a clear low emissivity (low-e) film that reflects some of the interior generated radiant energy back into the structure. The EG system magnifies the comfort and condensation control benefits by applying controlled amounts of electricity to the metal low-e film. This causes the temperature of the glass to rise [1]. This rise in temperature is what will be used to clear the test panels of any snow cover. The glass can be set to activate within the desired temperature range and the temperature of the glass is monitored through thermistors connected to the center of the pane. The EG covered panels provided to the team draw about 5A of current when the glass is active. Data collected over the course of the winter will tell if the power used by the glass counteracts the benefits of faster snow removal on the panels.
3. Results[edit | edit source]
3.1 Temperature Sensor Testing This experiment was performed in the fall of 2017. The EG system relies on one temperature sensor located in the center of the glass pane. The team aimed to confirm that the panel’s surface temperature was constant throughout in order determine if addition equipment was necessary and if the singular sensor could be moved to a more secure location. To ensure accurate temperature readings, a series of tests were conducted to compare glass surface temperatures at various locations. An Arduino temperature sensor was utilized for this experiment. After calibration, the EG was set to two different temperatures: 90℉ and 96℉. The surface temperature was recorded every five seconds for a two minute period at nine separate locations.
Once collected, the data was statistically analyzed to ensure temperature uniformity. To start, outlying data points were removed. Next, the 95% confidence interval was found for each location. If two confidence intervals overlap, they are considered to be statically the same.
Since there was a broad range of results, temperature locations were split into Top-Middle-Bottom and Left-Middle-Right to narrow the analysis. After repeating calculations, conclusions could be made about each zone. The top and middle sectors were the same but differed from the bottom. The left, middle, and right divisions are statistically different but nominally similar.
These statistical patterns are comparable to the 96℉ data set. Although both data sets showed variance between panel zones, it was only by about a degree or so. This narrow variance in temperature was found to be sufficient for the scope of this project. From this, it was determined that the panel temperature is fairly constant throughout, no additional temperature equipment was necessary for further experimentation, and the current sensor could be moved to the outer edge of each panel.
3.2 Snow Melt Testing To determine the viability of Energized Glass on solar panels for snow removal, multiple experiments with different objectives were conducted. This allowed for a plethora of data to better understand the effectiveness of the EG. Specifically, two arrays were used in all experiments, one with the EG and one without, to properly compare the impact EG has on snow removal.
Before any snow melt experiments could be done, a few systems needed to be put in place to be able to turn the EG panels on/off, adjust EG temperature settings, and record the power production from both arrays. To turn the EG panels on or off, the team decided to use the Minuteman system. This simple system allowed the team to use a VPN to connect to the KRC’s network remotely to control the power supplied to the EG. To adjust the EG panel temperatures, the team used the existing MiWi network that has been previously implemented. Power production data was tracked and stored for all experiments using a solar monitoring program known as Enphase. Enphase tracks and stores power production in five minute increments throughout the day for each individual panel within the control and EG arrays. With module control and data collection systems in place, the team could then continue forward with planned experiments.
Experiment 1 The goal of the first experiment in 2018 was to determine what kind of impacts the EG has on the solar panels’ power production. With this information, the team was able to determine if the EG would cause significant losses year round. To conduct this test, both sets of panels were cleared of any snow.
The power production values for each panel were manually entered into a spreadsheet in five minute increments where they could be easily analyzed. Power production was summed for each array to get total values for a given day.
After conducting an Analysis of Variance (ANOVA) and t-test, it was concluded that the two sets were statistically different. Next, the data was averaged for each five minute increment to accurately graph power production from each array over the course of the entire day. From this, the team concluded that the EG caused a 30% reduction in power production.
The 30% loss in power production can be attributed to in part, the type of glass used for the EG system. Typical solar panel glass has small ridges and prisms built into it. Each time light hits a glass surface, part of it is absorbed and refracted while the rest is reflected. By increasing the number of surfaces, solar panels are able to take in more light photons and produce more power. The EG system used the Pilkington Energy Advantage glass. As outlined in the product brochure, this glass is flat and transmits 73 to 75% of the solar energy that hits it [2]. This translates to a 25 to 27% reflection that does not reach the solar panel glass [2]. It was concluded that this type of glass caused the initial EG disadvantage. This was considered a tremendous discovery as it accurately explained why the EG panels were consistently producing less power than the control panels under identical conditions.
Experiment 2 The second overarching experiment of this semester was to determine the overall efficiency of the EG when all EG panels were powered on. This was completed twice, both times with about a half of an inch of snow coverage.
One goal of this experiment was to accurately compare the net power production of the EG panels to the power production of the control panels. Net power production is the power produced over the course of the day combined with the power required to heat the EG panels. Simply put, net power is the power produced minus the power required. The data taken from Enphase does not take the power required to heat the EG panels into consideration and therefore it had to be included in the calculations by the team. To calculate the power required to heat the EG panels, a software called MiWi was used to track energy consumption. All the panels were turned on, and the energy required to heat the EG panels was recorded and averaged across all panels. The result was that each panel would require 0.3409 kWh over the course of one hour, this was calculated to be the same as 28.4 W for five minutes. Unit conversions from kilowatts to watts and one hour to five minutes are as follows:
(0.3409 kWh / 1 h) * (1000 W / kW) * (5 / 60 minutes) = 28.4 W for every five minutes
Power requirements were determined for five minutes since that is the same interval used by Enphase. By doing this, the team was able to simplify net power calculations.
After measuring the power required by the EG, the team was able to determine the net power produced by the EG panels for each trial and carried through similar calculations to that of Experiment 1.
Although both tests showed a general trend of lowered efficiency, they followed different patterns to reach those final numbers. On February 15th the EG took a considerable amount of power to initially melt the snow, and the control power output was relatively low and stagnant. However on March 8th the EG required less power to melt the snow, and, unlike the results from February 15th, the control panels were able to naturally melt the snow shortly after the EG was cleared.
After reviewing the weather data and time-lapse photos provided by the KRC, the team determined two factors that most likely caused these differences between tests. The first factor is the outside air temperature before and during the experiment. On February 15th, the air temperature was greater than 32°F, so any snow on the panels would’ve had a relatively high water content and be a “wetter” snow. The air temperature then dropped below 32°F thus causing the wet snow to freeze into an icy layer before the EG was turned on. In comparison, the air temperature on the 8th remained well below 32°F, meaning the snow was drier and easier to melt. From this, one could conclude that the snow was more icy and harder to melt off on the 15th than on the 8th.
The second factor that most likely lead to the control panels being able to naturally melt off on March 8th but not February 15th was the amount of relative sunlight. By using the time-lapse photos provided by the KRC, the 15th was relatively cloudy and foggy while the 8th was very sunny. Over the course of the semester, direct sunlight has been shown to melt snow off of the panels quicker than diffused light.
After conducting these tests, the team realized that for both trials the EG was turned on longer than absolutely necessary and that power production could be optimized. To optimize power production, the team determined when the EG effectively removed enough snow using the timelapse photos provided by the KRC. Assuming that the EG was turned off immediately after the snow melted, the group applied the net power calculations only during the snow removal process. This optimized the net power production of the EG throughout the course of each day. With the EG power production optimized, the group was able to recalculate the potential efficiency of each test.
When optimized, the EG efficiencies alternated, and Test #1 on the 15th had a better performance than Test #2 on the 8th. As discussed earlier, this is possibly due to the different types of snow and sunlight. It is also important to note that the control panels remained completely covered for the entire day on February 15th and slowly melted off naturally on March 8th.
Over the course of the semester, the team noticed that snow tends to stick to the EG panels when they were not running. One final test was conducted to quantify this phenomena. The test consisted of allowing equal amounts of snow to fall onto the panels and letting them to melt naturally, without the assistance of the EG, over the course of a clear and sunny day. Both arrays were subjected to the same amount of snowfall and identical weather conditions with the only variable being the type of outermost glass. Without the support from the heated EG, the EG array took over ten times longer to melt snow than the control panel. It was concluded that the EG caused a 83% reduction over the course of the day, which includes the known 30% reduction.
4. Conclusions[edit | edit source]
From the experiments conducted this semester, the group was able to make a couple conclusions regarding the implementation of Energized Glass. The first conclusion is that the EG itself causes a 30% reduction in standard power generation. It was determined that this is due to the type of glass that the EG was installed on. Secondly, snow tends to stick to the EG when it is not heated. No further research was conducted to find the causes.
The team was not able to make a statistically valid conclusion when it came to the snow melt efficiency of the EG with the data collected during the semester. Initial data suggested that EG is not a viable option; however, after optimization, the possibility increased. Even though the efficiency increased when the EG was optimized, final results conflicted. One test showed that EG produced more power than the control panels while another showed less.
Few tests were conducted due to a number of limitations. An overarching limit was weather once the system was assembled. The number of opportunities to run an experiment was restricted by the amount and time of snowfall. In general this winter, snowfall would taper off towards the middle of the night. This proved to be an issue since the team could not measure power production at night time. In addition, not enough snow would accumulate on the panels to run a test. When producing power during the day, the panels were naturally warm. This warmth prevented snow from collecting on top of the control panels. Finally, the cameras that were used to monitor the panels would become covered with snow and ice up. This made it difficult to determine when to turn the EG on and off and to conduct tests in general.
5. Recommendations for Future Work[edit | edit source]
A common phrase in statistical analysis is “more data means more answers.” In order to have conclusive results, the team would need to conduct more experiments. The first type would be basic “all on” EG tests. This is what the team has done the entire semester. Once enough tests are completed and general trends can be identified, the team could move onto different tests. New experiments would include only turning the bottom row of EG panels on and setting the EG to various temperatures.
On top of more experiments, the group could try different analysis methods to better compare the the two arrays. For example, the power production differences could have been calculated using the percent of rated power. This is common within the solar industry.
In addition, more research regarding panel losses could be done. It was identified that the glass in the EG caused a power reduction, so the implementation of a different glass could increase the overall performance. Further research can also be done to determine if a second layer of glass is needed at all and if the EG system can be applied to a single layer of solar panel glass.
In general, the EG system needs to be put into a centralized system for realistic and practical purposes. Currently three systems are running to use and analyze the EG panels. One system turns the panels on (MinuteMan), one system changes EG temperature settings (MiWi), and one system records power production without considering power required to heat the panels (Enphase). The team had to learn how to use all systems, and the information from these systems then had to be manually entered into a spreadsheet and analyzed. Realistically, a consumer does not have the time to carry out such a process. The need for a centralized program that can turn on the EG, change the EG temperature setting, and record EG power production that considers the power required to heat the EG panels is greatly needed.
Lastly, one huge drawback the team experienced throughout the winter was the limited range of the MiWi system. The KRC is located approximately 50 feet from the EG panels and the MiWi system would not work at this range. The only way the team could use the MiWi was by standing within five feet of the EG panels with a wireless laptop, which is not practical in the winter months.
7. References[edit | edit source]
[1] Lundahl, D. (n.d.). Welcome to Energized Glass - Perfecting Radiant Energy. Retrieved December 12, 2017, from http://www.energizedglass.com/
[2]Pilkington. Pilkington Energy Advantage. Pilkington Energy Advantage, March 2012, http://assetmanager-ws.pilkington.com/fileserver.aspx?cmd=get_file&ref=USA175&cd=cd
