Examining the Relationships between Stationary Occupancy and Building Energy Loads in US Educational Buildings–Case Study

Round 1

Reviewer 1 Report

This paper proposed a new and simple approach to infer stationary occupancy. The case study investigated the relationship between building occupancy and building energy loads such as electricity, cooling, and heating loads, using correlation and multiple regression analyses. I find the work interesting, but I think the authors should answer the following questions.

In order to achieve greater accuracy in the source data, the authors should have used a data logger to monitor outdoor and indoor temperature data in the case study. Given that the source they use is far from the building, the input variables may be altered.

The first citation in a scientific paper should not come from a website. Line 33.

In the conclusions, the accuracy between the simulated and actual consumption is described. Line 359. Currently, administration offices have temporary occupants who do not use the Internet. Have this percentage and its impact on results been taken into account in the methodology?

Author Response

Point 1: This paper proposed a new and simple approach to infer stationary occupancy. The case study investigated the relationship between building occupancy and building energy loads such as electricity, cooling, and heating loads, using correlation and multiple regression analyses. I find the work interesting, but I think the authors should answer the following questions.

Review: We have submitted our answers to your questions. We appreciate your comments.

Point 2: In order to achieve greater accuracy in the source data, the authors should have used a data logger to monitor outdoor and indoor temperature data in the case study. Given that the source they use is far from the building, the input variables may be altered.

Review: The outside temperature data were collected from the U.S. Climate Data website and ASU Metabolism system. One of the climate data stations, and ASU metabolism data are located near the building. As a result, the research team feels that it is not necessary to use data from data logger.

Point 3: The first citation in a scientific paper should not come from a website. Line 33.

Review: Deleted

Point 4: In the conclusions, the accuracy between the simulated and actual consumption is described. Line 359. Currently, administration offices have temporary occupants who do not use the Internet. Have this percentage and its impact on results been taken into account in the methodology?

Review: The impact is negligible. Regular temporary occupants, e.g. students, do use the internet when they are in the building. Non-regular temporary occupants who do not use the internet are small in numbers as they would not be bothered to log into the internet. The internet would require prior registration and it is cumbersome if one only needs it once. The detail is explained in lines 242 to 253 (Types 1 to 3 of occupants).

 

 

Author Response File: Author Response.docx

Reviewer 2 Report

Lines 61-62: The assumption of a maximum occupancy is used for HVAC design not for energy consumption. The sentence 61-62 should then be corrected.

Lines 63-67. Again, this is something very common in the design stage but that's not what is nowadays done for energy consumption estimation. 

 

Continuously, there is a misunderstanding between design criteria and energy estimation  

 

Lines 104-105: Please clarify "Residential and office buildings have almost the same occupancy type..."
In fact, both the period of the day and even the intensity of occupancy in residential and office buildings are very different

This is also repeated in lines 128-130. Pease keep order in the text.

 

Lines 139-180 (and before) continuously insist on the idea that there are methods to estimate the occupancy but they do not allow to differentiate stationary and non-stationary occupants.

In my opinion it is necessary to previous explain the differences and the consecuence of that.

 

Figure 2 vs Figure 3 (and Table 1 vs table 2  ) show daily vs hourly values without any explanation and analysis in the text.

Additionally, why we can find heating and cooling simultanously for the same range of outdoor air temperature (30 to 80)

 

Lines 238-240 : The conclusion in this line is not correct in my opinion "However, even if the number of occupants increase or decrease, cooling and heating loads are immune to occupancy."

All studies show an increase of cooling load with the number of occupants and the contrary for heating. Both due to the heat released by those occupants.

 

 

 

 

 

Author Response

Point 1: Lines 61-62: The assumption of a maximum occupancy is used for HVAC design not for energy consumption. The sentence 61-62 should then be corrected. Lines 63-67. Again, this is something very common in the design stage but that's not what is nowadays done for energy consumption estimation.

Review: 

Lines 61-62: Replaces “research” with “design” in line 60.

Lines 63-67: While the research team understands that load estimation is done at the design phase, and not to estimate energy consumption, the research objective is to connect occupancy and energy consumption using internet usage data. This discussion is thus necessary as the research objective is to make potential changes to the design procedure.

Point 2: Continuously, there is a misunderstanding between design criteria and energy estimation.  This is also repeated in lines 128-130. Pease keep order in the text. Lines 139-180 (and before) continuously insist on the idea that there are methods to estimate the occupancy, but they do not allow to differentiate stationary and non-stationary occupants.  In my opinion it is necessary to previous explain the differences and the consequence of that.

Review: Line 128-130: I think the statement is misinterpreted. The research title is “relationship between stationary occupancy and building energy load”, and thus we need to develop the relationships between occupancy and building energy load. We are not discussing how energy estimation should be estimated using occupancy, our research is connecting occupancy and energy load. Our discussion should be in order. We are attempting to establish the connection between stationary occupancy and building energy loads, and we could use wi-fi to estimate occupancy and then energy load. Occupancy estimate is an objective in this research and thus cannot be left out. 

Point 3: Figure 2 vs Figure 3 (and Table 1 vs table 2) show daily vs hourly values without any explanation and analysis in the text.

Review: The new discussions can be found from lines 208 to 221.

Point 4: Additionally, why we can find heating and cooling simultaneously for the same range of outdoor air temperature (30 to 80).

Review: This is the practice for building cooling and heating practices in many parts of the world. Cooled air is often re-heated if the temperature is too low, and the same is done to heated air. This is a very common practice for buildings where several buildings share a common heating and cooling system. For building with individual HVAC system, their reheating or re-cooling is done at the outlet, and thus they do not have similar heating or cooling load simultaneously. Their energy is considered as electricity consumed by the building, and not as cooling/heating load. The reheating and re-cooling processes ensure that the air is let out at the right temperature.

Point 5: Lines 238-240 : The conclusion in this line is not correct in my opinion "However, even if the number of occupants increase or decrease, cooling and heating loads are immune to occupancy."

Review: Lines 238 to 240 accurately describes the findings of the research. The research results are statistically and qualitatively proven accurate for the case building. As indicated by Table 3, where there are little to no relationships between cooling/heating loads and wi-fi users (-0.146 and -0.233). Electrical load is highly correlated to wi-fi users (0.848). There is no statistical correlations between cooling and heating loaders and users. The building HVAC system continues to operate regardless of the total number of occupants is in the building. If a commercial building is designed for 100 people, the HVAC system is always turned on for 100 people, without regards to the total number of people occupying the building (1 to 120 people). Unlike other appliances, HVAC system cannot automatically adjust itself to small changes in building occupancy, and thus HVAC is “immune” to small changes in occupancy. Further studies are currently conducted to understand the reasons for this insignificant relationship, and more results will be published over the years.

Point 6: All studies show an increase of cooling load with the number of occupants and the contrary for heating. Both due to the heat released by those occupants.

Review: The analysis on this paper shows otherwise, both statistically and qualitatively (see results in Table 3). It depends on the types of buildings, and how HVAC system is operated. Studies have also shown otherwise, as indicated in the literature review. Further studies are currently underway to understand the reasons behind such correlations.

Reviewer 3 Report

This paper has a lot of strengths. The research questions are clearly quoted (lines 132-138) and the state of the art is well conducted at different levels. The method and data processing are clearly exposed, although I am not an expert in statistical issues.

I would like to point out some elements that concern research design and can be improved. The management conditions of the HVAC systems mentioned in lines 342-347 should be explained in the “case study building” paragraph and not only in the discussion on the results. Likewise, something should be said about the performance of the envelope that influences the correlation between temperature and hourly heat load.

More attention should be paid to the terms used in the text and captions, specifying them or using them appropriately, in particular: energy load and energy consumption, permanent, temporary, stationary, long term and short term occupancy.

Overall the paper provides an interesting contribution especially for educational buildings on the topic of the use of Wi-Fi and Ethernet data traffic as a proxy for total and variable occupancy to improve the energy management of a building (see lines 105-109, 178-180). Although it remains to better investigate the relationship between occupancy and heating and cooling loads that undoubtedly are more important than the case study has been able to highlight. For this reason I would recommend to modify the final sentence of the abstract (lines 27 and 28) stating that we are referring to the case study results.

Author Response

Point 1: This paper has a lot of strengths. The research questions are clearly quoted (lines 132-138) and the state of the art is well conducted at different levels. The method and data processing are clearly exposed, although I am not an expert in statistical issues.

Review: Appreciate your comment.

Point 2: I would like to point out some elements that concern research design and can be improved. The management conditions of the HVAC systems mentioned in lines 342-347 should be explained in the “case study building” paragraph and not only in the discussion on the results. 

Review: We agree and added the statement to the “case study building” paragraph. It is now in lines 194 to 200.

Point 3: Likewise, something should be said about the performance of the envelope that influences the correlation between temperature and hourly heat load.

Review: A new section titled “Research Objectives”. A new statement is added in lines 140-142.

Point 4: More attention should be paid to the terms used in the text and captions, specifying them or using them appropriately, in particular: energy load and energy consumption, permanent, temporary, stationary, long term and short term occupancy.

Review: We have revised the wordings in the manuscript.

Point 5: Overall the paper provides an interesting contribution especially for educational buildings on the topic of the use of Wi-Fi and Ethernet data traffic as a proxy for total and variable occupancy to improve the energy management of a building (see lines 105-109, 178-180). Although it remains to better investigate the relationship between occupancy and heating and cooling loads that undoubtedly are more important than the case study has been able to highlight. For this reason, I would recommend to modify the final sentence of the abstract (lines 27 and 28) stating that we are referring to the case study results.

Review: The revised statement: “The statistical and qualitative analyses indicated that there was no significant relationship between occupancy and thermal loads, such as cooling and heating loads.”

 

Reviewer 4 Report

 The paper discusses the impact of the building occupancy, by using existing network infrastructure, i.e. Wi-Fi and wired Ethernet, to energy consumption and heating and cooling loads.

Considering that the paper talks about stationary and non-stationary occupancy, maybe it would be useful to adapt the title. "This case study proposes to infer the stationary occupancy in buildings using wired Ethernet connections."- in some countries Wi-Fi internet have very good speeds, thus the wired Ethernet is used only by a few. Thus, this methodology will not yield good results.   "In general, wired Ethernet is faster, more stable, and delivers more consistent speeds than wireless. However, short-term occupancy use only wireless internet connections, because they do not have access to the wired Ethernet, and most of them do not carry an Ethernet patch cable for using wired Ethernet, though wireless is more inconvenient to use. " Even long term occupants use wireless internet during the office hours, if the speed is good enough. In many European countries the internet has a much higher speed compared to North America. "The outside temperature data were collected from the U.S. climate data website  (http://www.usclimatedata.com); electricity, cooling, and heating load data were collected from the 188 ASU Metabolism system, "- how does the exterior temperature data vary in that region? How did the exterior temperature vary on the assessed period of time? How did the loads vary on the assessed period? Please describe how the Pearson correlation analysis and regression analysis function. Please provide some information regarding the structure and building envelope of the assessed building. Also, some information regarding the building system. " In most cases, there are far fewer occupants on weekends and holidays than during weekdays, so cooling and heating loads should also have been far lower, like the data traffic in Figure 1, "- how is the HVAC of the building calibrated? Maybe this is due to that. For all figures, please mention the unit measure for the plotted values (e.g. temperatures, cooling loads, heating loads, and others) How can one understand the plus or minus values from tables  as table 1? For figure 3, the plotted values are for the entire assessed period? " However, even if the number of occupants increase or decrease, cooling and heating loads are immune to occupancy."- could it be related to the building envelope characteristics or to other settings of the HVAC system? Can it be some measurement errors? Please mention a similar case met in current literature. "represents weekdays after the beginning class"- please rephrase. It refers to the moment when classes begin? Line "This implies that the electricity load is more affected by stationary occupancy than by non-stationary."- this is obvious. I would recommend other conclusions that resulted from the research. "Thus, it is surmised that the students’ influence on electricity load was not significant"- maybe due to the fact that their laptops and devices were already charged, so no need of electricity? thus, the results can be questionable. "According to the results, there was no correlation between the thermal loads and occupancy."- I would argue that. There is not data available in the paper regarding the way that the HVAC system interacts with the occupants, or is calibrated, thus this assumption can be far from the truth. "For example, during occupied periods, if there are no Wi-Fi connections or wired data traffic in certain rooms, the room does not need cooling or heating. And if there are a few Wi-Fi connections or a little data traffic in a room, the HVAC system only needs to reduce the  loads, and vice versa. " - it would be interesting and useful to see in the paper, the correlation in reducing loads connected by considering this hypothesis. "It is expected that the research results can be utilized practically. "- I do not consider that the research results can be utilized practically. That are still missing information that can give a better picture of the influences between different investigated parameters. " So, by making use of the data, it can be used in various ways, such as optimizing the use of building energy and water, to improve occupant comfort."- to a certain degree it can be applied, but not on an international scale. The US case does not apply, from the usage of internet point of view to all countries.

Author Response

Point 1: The paper discusses the impact of the building occupancy, by using existing network infrastructure, i.e. Wi-Fi and wired Ethernet, to energy consumption and heating and cooling loads.

Review: You described it accurately.

Point 2: Considering that the paper talks about stationary and non-stationary occupancy, maybe it would be useful to adapt the title. "This case study proposes to infer the stationary occupancy in buildings using wired Ethernet connections."- in some countries Wi-Fi internet have very good speeds, thus the wired Ethernet is used only by a few. Thus, this methodology will not yield good results.   "In general, wired Ethernet is faster, more stable, and delivers more consistent speeds than wireless. However, short-term occupancy use only wireless internet connections, because they do not have access to the wired Ethernet, and most of them do not carry an Ethernet patch cable for using wired Ethernet, though wireless is more inconvenient to use. " Even long term occupants use wireless internet during the office hours, if the speed is good enough. In many European countries the internet has a much higher speed compared to North America. 

Review: 

A new section is written to address this topic and they can be found in lines 207 to 267. It gives an extensive overview of why internet speed has no correlations with energy consumption and thus is not considered in this research. It also answers many of the comments that you have.

On your comment about the different internet speed between the USA and Europe (lines 251 to 260), your example is based on the average internet speed by country, not based on the available internet speed and states in the United States and individual states and regions. Please review the two datasets for internet speed:

https://www.fastmetrics.com/internet-connection-speed-by-country.php#top-10-comparison-2018

https://www.fastmetrics.com/internet-connection-speed-map-usa.php

US internet speed data from 2015 shows that nearly half the states have higher average internet speed than most European countries. The average internet speed in the USA is reduced by the rural states as the demand for higher speed internet is reduced among the states with the largest agricultural and poorer communities. The US has a greater land mass than Europe and thus it is not reliable to compare the entire United States with individual European country. Some states have larger land mass and population than many European countries.

On the comment on Ethernet is used by a few, I don’t think that is an accurate statement. Most homes and businesses continue to rely on ethernet as it still provides much faster internet. For wi-fi, they are usually transmitted from Ethernet through routers as wi-fi signals. Essentially, the wi-fi our computers use comes from Ethernet. For example, most computers on ASU campus are still connected to the Ethernet, and the wi-fi signals are generated from the Ethernet. Even if Internet Service Providers (ISP) provide wi-fi services throughout the city or as a direct subscription service for customers, the wi-fi is generated from Ethernet. Many demanding tasks like collaborative design (Boeing and Airbus), datamining and gaming still require direct wired connection to the Ethernet, and thus most computers at the office are wired rather than wi-fi. So long term occupants at the investigated building are connected via Ethernet.

Internet Service Providers (ISP) in the United States provide a wide range of internet speed for subscription, and the fastest of 3 Gbps to the slowest at 5 Mbps. The average internet speed is based on what is available to the specific regions, for example, some less populated regions may not get the fastest internet as ISP do not invest in the region to upgrade the internet speed. ISP focuses on the densely populated cities and highly developed areas. Regions with high density of tech industry have far greater internet speed than regions that focus on agriculture and mining.

In addition, there are technology limitation, i.e. sacrificing speed for distance and vice version – 2.5ghz versus 5.0ghz, dual band wi-fi. This limitation is not limited to the United States.

The Arizona State University’s average internet speed is far greater than the entire country, i.e. 3 Gbps on Ethernet and about 400-500 Mbps for short range wi-fi, and 50 Mbps or higher on long range wi-fi.

I am glad that you raise this point as this gives us the opportunity to insert a new section in the manuscript so that we could let readers understand why we did not consider internet speed and energy consumption.

Point 3: "The outside temperature data were collected from the U.S. climate data website  (http://www.usclimatedata.com); electricity, cooling, and heating load data were collected from the 188 ASU Metabolism system, "- how does the exterior temperature data vary in that region? How did the exterior temperature vary on the assessed period of time? How did the loads vary on the assessed period? 

Review: 

The period of study is 3.5 years and conducted on a new building. Two of the three researchers occupy the building. The building has extensive external and internal temperature and energy consumption data from the ASU Metabolism system. The data from US climate data and ASU Metabolism are like one another, even though there are some slight differences, the plots do not show any significant differences. As a result, exterior temperature variation is not taken into consideration.

The scattered plots have taken the relationship between temperature and time into consideration. Internet use would determine the number of occupants in the building, and thus, the research objective is to focus on how occupancy affects building energy load. Thus, load is a factor of occupant. We should isolate the other factors. In addition, the internet use reflects changes in the number of occupants in the building.

Point 4: Please describe how the Pearson correlation analysis and regression analysis function.

Review: Lines 196 to 202 include a new paragraph.

Point 5: Please provide some information regarding the structure and building envelope of the assessed building.

Review: Building occupancy and outdoor temperature are the two factors studied in this research as indicated in the research objectives, and the focus is to understand if wi-fi would exhibit a relationship. As a result, other energy factors, such as envelope, structural materials, equipment efficiency etc. are isolated. To isolate the factors, the dataset was separated into two sets, one with extreme days and one without. Separate analysis was conducted on both, and no significant relationship is found using linear regression. We did not post the result in the research paper as we would have surpassed the wording limitation if we do. We did mention this in the manuscript in lines 295 to 309.

Point 6: Also, some information regarding the building system. " In most cases, there are far fewer occupants on weekends and holidays than during weekdays, so cooling and heating loads should also have been far lower, like the data traffic in Figure 1, "- how is the HVAC of the building calibrated? Maybe this is due to that.

Review: The statement you cited is a description of what happened during those hours and we discuss that in the manuscript, so yes, it is due to that.

Point 7: For all figures, please mention the unit measure for the plotted values (e.g. temperatures, cooling loads, heating loads, and others) How can one understand the plus or minus values from tables  as table 1? For figure 3, the plotted values are for the entire assessed period? " However, even if the number of occupants increase or decrease, cooling and heating loads are immune to occupancy."- could it be related to the building envelope characteristics or to other settings of the HVAC system? Can it be some measurement errors? Please mention a similar case met in current literature. "represents weekdays after the beginning class"- please rephrase. It refers to the moment when classes begin? Line "This implies that the electricity load is more affected by stationary occupancy than by non-stationary."- this is obvious. I would recommend other conclusions that resulted from the research. "Thus, it is surmised that the students’ influence on electricity load was not significant"- maybe due to the fact that their laptops and devices were already charged, so no need of electricity? thus, the results can be questionable. "According to the results, there was no correlation between the thermal loads and occupancy."- I would argue that. There is not data available in the paper regarding the way that the HVAC system interacts with the occupants, or is calibrated, thus this assumption can be far from the truth. "For example, during occupied periods, if there are no Wi-Fi connections or wired data traffic in certain rooms, the room does not need cooling or heating. And if there are a few Wi-Fi connections or a little data traffic in a room, the HVAC system only needs to reduce the  loads, and vice versa. " - it would be interesting and useful to see in the paper, the correlation in reducing loads connected by considering this hypothesis. "It is expected that the research results can be utilized practically. "- I do not consider that the research results can be utilized practically. That are still missing information that can give a better picture of the influences between different investigated parameters. " So, by making use of the data, it can be used in various ways, such as optimizing the use of building energy and water, to improve occupant comfort."- to a certain degree it can be applied, but not on an international scale. The US case does not apply, from the usage of internet point of view to all countries.

Review: This is an extremely complicated comment and touches many aspects of energy consumption research. However, these comments are not the focus of this research and are not to be addressed by the research objectives. The objective of this research is to understand if wi-fi data could be used to estimate occupancy, and thus be used to estimate energy use. Many of these factors require extensive research and thus would result in more publications.

It is extremely difficult to collect accurate occupancy data. The research team attempted in the past to collect such data using sensors and plug loads and it goes nowhere. None of the research objectives is focused on making the energy consumption estimation more accurate. The novel idea and thus the research contribution are to use wi-fi data to correlate with energy consumption data. If proven accurate and reliable, this would replace costly and unreliable methods of counting the number of occupants.

Some of your recommendations are good, however, they could only be established with extensive and multi-year research effort by numerous researchers. Many are part of our plans running into the future.

Some of these data are nearly impossible to collect,

for example, knowing which laptop is being charged or not. There is no way one could measure the individual energy consumption of all plugs located in a building, you could only do samples on select locations. There are 287,841 plugs, 12,198 computers, 24 coolers and 12 heaters among other electrical equipment in the select building. Our proposed approach is a more cost and time effective one, if proven successful and that is our contribution. To measure energy consumption individually throughout the building, it would be an extensive effort that require a lot of manpower. This US case is definitely application anywhere in the world as most part of the world has already wi-fi data available. However, you have given us good suggestions and we are currently planning to expand our studies and employ more researchers to cover these areas.

As for your assumption for internet use, it should be applicable to all countries just the habit of internet use should be adjusted.

Round 2

Reviewer 2 Report

In my opinion, and considering the authors answer to point 5 and 6, it should be convenient to clarify in the conclusion section that the reason to not find a correlation between occupancy and energy consumption for space heating or space cooling is due to the fact that in this particular building the HVAC system has a on/off set independently to the indoor heat gains.

I must confess I think this is really difficult to understand since most HVAC system are controlled by an indoor temperature sensor. The signal from this sensor is clearly affected by occupancy and then, at the end, the occupancy level is affectin the running hours and power of the HVAC system (even if its in this indirect way)

Author Response

In my opinion, and considering the authors answer to point 5 and 6, it should be convenient to clarify in the conclusion section that the reason to not find a correlation between occupancy and energy consumption for space heating or space cooling is due to the fact that in this particular building the HVAC system has a on/off set independently to the indoor heat gains.

Answer: It was added. “In addition, HVAC systems do not consider whether the building is partially occupied; there are only “occupied” or “unoccupied” periods of the day (Li et al., 2012). The object building of this research is operated in a similar way. According to the ASU Facilities Services, the building HVAC system is controlled by periods that are set by manager, and there are only “occupied,” “unoccupied,” “preoccupancy,” and “setback” periods. This system is too rough and simple, so it is not helpful for energy saving. Thus, there seems to be high energy saving potential if HVAC loads can be adjusted automatically based on real-time occupancy information (Liao and Barooah, 2010; Li et al., 2012).”

 

I must confess I think this is really difficult to understand since most HVAC system are controlled by an indoor temperature sensor. The signal from this sensor is clearly affected by occupancy and then, at the end, the occupancy level is affectin the running hours and power of the HVAC system (even if its in this indirect way)

Answer: You are also correct. The temperature sensor works well, but sometimes it doesn’t. For example, during the daytime in summer, room’s temperature will increase, and HVAC will be turned on even if there is no occupancy in the room. So, if we can detect there is a person or not in a room, we can save energy by preventing turning on the HVAC. Like this, because the most of sensors have limitations, so the sensors can be combined and be complementary to each other.

 

Please see the attachment.

Reviewer 4 Report

The plots still do not have Unit Measure. The temperature is measured in F or C? The cooling loads are in MW, kWh, kwh/sqm?  The review mentions "Lines 196 to 202 include a new paragraph" but i do not see the lines mentioned. Also, "A new section is written to address this topic and they can be found in lines 207 to 267." the line number stops at 195 and continues at 251. I am not sure if the version of the manuscript if the manuscript is acurate or not. Thus, please provide the manuscript with the correct numbering in order for an accurate judgement for the revised version.

Author Response

The plots still do not have Unit Measure. The temperature is measured in F or C? The cooling loads are in MW, kWh, kwh/sqm? 

Answer: All the unit measure was added.

 

The review mentions "Lines 196 to 202 include a new paragraph" but i do not see the lines mentioned.

Answer: It was added. “Pearson correlation and linear regression can determine if two numeric variables are significantly linearly related. A correlation analysis provides information on the strength and direction of the linear relationship between two variables, while simple linear regression analysis estimates parameters in a linear equation that can be used to predict values of one variable based on the other.”

 

Also, "A new section is written to address this topic and they can be found in lines 207 to 267." the line number stops at 195 and continues at 251. I am not sure if the version of the manuscript if the manuscript is accurate or not. Thus, please provide the manuscript with the correct numbering in order for an accurate judgement for the revised version. 

Answer: The new lines are from 204 to 260, and the fonts are in red. Thanks.