For the five studies that satisfy our criteria, the electricityintensity of transmission networks has declined by factor of170 between 2000 and 2015

2Example of daily variation of Internet traffic in 2012, based on number of page views per 15-minute interval for part of theAkamai network (Peill-Moelter 2012, reprinted with permission).

A white paper released byCisco (2015) predicts Internet traffic growth of 42% per yearto 2020.

the broader trends identified by Koomey andcolleagues (2011) and Koomey and Naffziger (2015, 2016) aresuggestive of the rates of change we would expect to see innetworking devices constructed from silicon microprocessorsand related components.

Williams and Tang (2012)estimate the carbon intensity

Estimatesbased on specific or state-of-the-art equipment, such as Baligaand colleagues (2009), omit the less efficient legacy equip-ment (i.e., equipment with higher electricity use per GB ofdata transferred) in use within country-wide Internet networks,resulting in a substantial underestimate of electricity intensityat the lower end of the observed range (0.004 kWh/GB for2008).

Existing estimates for the electricity intensity of Internetdata transmission, for 2000 to 2015, vary up to 5 orders of mag-nitude, ranging from between 136 kilowatt-hours (kWh)/GBin 2000 (Koomey et al. 2004) and 0.004 kWh/GB in 2008(Baliga et al. 2009). While increased efficiency over time canaccount for 2 orders of magnitude of this variation (based onresults presented below), alone it does not explain the spreadof results.

For ex-ample, Mayers and colleagues (2014) applied electricity in-tensity estimates as part of an LCA study comparing differentmethods of games distribution, concluding that the carbon-equivalent emissions arising from an Internet game download(for an average 8.8-gigabyte [GB] game) were higher than thosefrom Blu-ray Disc distribution in 2010

This article derives criteria to identify accurate estimates over time andprovides a new estimate of 0.06 kWh/GB for 2015.

Pollution, broadly, is the number one source of unrest and citizen dissatisfaction, and it’s actually an essential threat to the rule of the Chinese Communist Party because they have to do something about it to keep their people content.

In the late 1970s, it cost $100 a watt for solar panel material, but the price has dropped 300-fold over the last 40 years. The first 100x price drop didn’t matter because solar was still more expensive than coal or gas. So all through that incredible price drop, people could say, “It’s a toy. It’s never going to make sense.”

The AutoGrid Flex platform interfaces with a wide variety of IoT devices, from residential to industrial-scale energy applications. In addition to energy-consumption data, typical residential appliances may also provide telemetry about air temperature, humidity, water temperature, and occupancy. Industrial devices often generate a variety of interesting process-specific data, but some of the most common and useful measurements include wind speed, solar irradiance, and thermal limits. These data streams can be leveraged by the AutoGrid machine learning algorithms to enhance forecasting and optimization of flexible energy resources throughout the network.

If, for example, an OhmConnect consumer saves one kilowatt hour (kWh) of electricity, the California ISO will reward OhmConnect as if that consumer generated one kWh. OhmConnect in turn passes a significant portion of that savings to its end user.

Winn said that solar plant operators can also attach thermal cameras to drones to help identify solar cells that are less efficient, perhaps even broken: A solar cell that’s absorbing all the energy and producing electricity is going to be much cooler than one that is not.

The Heila IQ box runs powerful software that presents an abstract view to the operator. Instead of directly controlling the individual assets, the operator describes higher-level goals and constraints such as “reduce emissions” or “avoid using gas-based generators because they are expensive.” Then, as the microgrid is operating, the Heila IQ automatically controls the assets to try to optimize for these goals and satisfy the constraints. Later, if the operator adds new assets to the microgrid, they don’t need to configure the individual assets or try to rebalance the system. As long as they specify the higher-level goals and constraints, the Heila IQ-based microgrid continues to control the assets appropriately.

Smaller data centers—servers stashed in closets or rooms in office buildings under 5,000 square feet—barely apply these efficiency strategies. That’s how small and medium-sized data centers end up consuming 49 percent of the electricity used in U.S. data centers each year, despite owning just 40 percent of the total number of servers, according to a 2014 report by the nonprofit Natural Resources Defence Council (NRDC).

Background CSS video & Responsive Video can now be a “thing”.

Wireless communications has been recognized as akey enabler to the growth of the future economy. There is anunprecedented growth in data volume (10x in last 5 years) andassociated energy consumption (20%) in the Information andCommunications Technology (ICT) infrastructure.The challenge is how to: meet the exponential growth in datatraffic, deliver high-speed wide-area coverage to rural areas,whilst reducing the energy consumed. This paper focuses on thecellular wireless communication aspect, which constitutes approx-imately 11% of the ICT energy consumption. The paper showsthat with careful redesign of the cellular network architecture,up to 80% total energy can be saved. This is equivalent to saving500 TWh globally and 1.4 TWh in the United Kingdom.

Data usage on the internet is estimated to be 20,151 PetaBytes per month (Cisco 2011). This is equivalent to 241 billion GB per year. Applying these figures to the average power estimate yields a figure of 5.12 kWh per GB.

An example transmission activity might begin on a desktop computer when an end user requests to download a song.

Many people are familiar with Moore’s law, which states that computational speeds are increasing at an exponential pace (Wikipedia 2012). There is also a corollary to this relationship known as Koomey’s law, which states that computational energy efficiency is also increasing at an exponential rate (Koomey 2009).

Our major finding is that the Internet uses an average of about 5 kWh to support the utilization of every GB of data, which equates to about $0.51 of energy costs. Only 38% of those costs are borne by the end-user, while the remaining costs are thinly spread over the global Internet through which the data travels; in switches, routers, signal repeaters, servers, and data centers (See Figure 1 below). This creates a societal “tragedy of the commons,” where end users have little incentive to consider the other 62% of costs and associated resources.

Indeed, our national companies further increased their shares of electricity from renewable energy, coming to a total group-wide average of almost 33 percent by the end of 2016.

The lower the frequency of the band the further it can travel, so the 800MHz band is the most adept of the three at travelling over long distances, which means users can get a 4G signal even when they’re a long way from a mast. This becomes particularly useful in rural areas where masts are likely to be quite spread out.

Infrastructure Electricity Use for All Scenarios

The formulas in the “Redundancy” column represent the total number of servers needed for a data center containing N functional servers. For example, redundancy of “N+1” means that there is one redundant server present in each data center, while redundancy of “N+0.1N” means that there is one redundant server for every 10 functional servers. For data centers where the number of redundant servers scales with server count (i.e. closets, mid-tier, and high-end enterprise), consolidation of servers reduces the number of redundant servers required.

The percent decrease in service provider data centers is assumed to be smaller because these data centers tend to have a lower rate of inactive servers due to better management practices that avoid the institutional problems of dispersed responsibility between IT and facility departments which often plagues internal data centers.

Infrastructure savings result from the reduced amount of IT equipment that require cooling and electrical services as well as the decrease in industry-wide average PUE, brought down by the growth in data centers with very low PUE values (i.e., hyperscale data centers).

Historical Data Center Total Electricity Use

Total Electricity Consumption by Technology Type

PUE by Space Type

Consequently, smaller data centers are still being measured with PUE values greater than 2.037 while large hyperscale cloud data centers are beginning to record PUE value of 1.1 or less.

Total Server Installed Base by Data Center Space Category

Total U.S. Data Center Network Equipment Electricity Consumption

Total U.S. Data Center Storage Electricity Consumption

The values shown in Table 1 represent the average of active servers, and therefore the inclusion of inactive servers (assumed to be 10% of internal and 5% of service provider and hyperscale data centers) slightly lowers the overall averages.

Volume Server Installed Base 2000-2020

Similar to previous U.S. data center energy estimates,12345 this study uses data provided by the market research firm International Data Corporation (IDC) to derive numbers of data center servers, as well as storage and network equipment, installed in the United States. Power draw assumptions are then applied to the estimated installed base of equipment to determine overall IT equipment energy consumption.

Figure ES-1 shows that these five scenarios yield an annual saving in 2020 up to 33 billion kWh, representing a 45% reduction in electricity demand when compared to current efficiency trends.

The resulting electricity demand, shown in Figure ES-1, indicates that more than 600 additional billion kWh would have been required across the decade.

From 2000-2005, server shipments increased by 15% each year resulting in a near doubling of servers operating in data centers. From 2005-2010, the annual shipment increase fell to 5%, partially driven by a conspicuous drop in 2009 shipments (most likely from the economic recession), as well as from the emergence of server virtualization across that 5-year period. The annual growth in server shipments further dropped after 2010 to 3% and that growth rate is now expected to continue through 2020.

The new high-­‐speed LTE networks that accelerate themobile Internetrequireup to three times more data per hour per task compared to the previousslower 3G networks, and thus more energy.43And compared to 2G networks, LTEenergy consumption is 60 times greaterto offer the samecoverage.

We measured the mix of advertising and editorial on the mobile home pages of the top 50 news websites – including ours – and found that more than half of all data came from ads and other content filtered by ad blockers. Not all of the news websites were equal.

It was also achieved with the support of some of Scottish biggest industries including the whiskey industry.

Yes we do have a Wordpress plugin, available here: http://wordpress.org/extend/plugins/cloudinary-image-management-and-manipulation-in-the-cloud-cdn/. While you don't need to install any image software on your server, you will need to register for a (free) Cloudinary account to use the plugin and start uploading images to the cloud.

ImageOptim makes images load faster Removes bloated metadata. Saves disk space & bandwidth by compressing images without losing quality.

Figure 4: Typical diurnal cycle for traffi c in the Internet. The scale on the vertical axis is the percentage of total users of the service that are on-line at the time indicated on the horizontal axis. (Source: [21])

Our energy calculations show that by 2015, wireless cloud will consume up to 43 TWh, compared to only 9.2 TWh in 2012, an increase of 460%. This is an increase in carbon footprint from 6 megatonnes of CO2 in 2012 to up to 30 megatonnes of CO2 in 2015, the equivalent of adding 4.9 million cars to the roads. Up to 90% of this consumption is attributable to wireless access network technologies, data centres account for only 9%.

There is a newfactor; at the core of the global Internet all of traffic ultimately moves through high-­‐speed fiber-­‐optic Internet exchange points (IXPs). Engineers have achieved a10,000 fold improvement in IXPspeedssince the 1980s.111But the rate of improvement hit a physics wall around 2005. Future traffic growth will require new, different and more hardware.

Suchhighlydispersed networks may increaseoverallenergyusewhen counting boththe in-­‐building network energy, and the energy to manufacturemillions of picocells.

AnEU project directed at reducing cellular energy use –because the “networks are increasingly contributingto global energy consumption” -­‐-­‐identifiedtechnologiesthat can yielda 70% reduction in energy per byte transported.107But, global mobile traffic isforecast to rise 20-­‐fold in five years

Listening just onceto a song stored in the Cloudusesless energy than purchasing and shippinga CD, taking into account manufacturing and transport energy. Listeningto the song a couple of dozen times leads tomoreoverallenergy used,largelybecause ofgreater use of the networks.105The Cloud uses more energy streaminga high-­‐def moviejust once than does fabricating and shippinga DVD.

Most current estimates likely understate global ICT energyuse by as much as 1,000 TWhsince up-­‐to-­‐date data are unavoidably “omitted”. At the mid-­‐point of the likely rangeof energy use, the total ICT ecosystemnowconsumesabout 10% of world electricity supplied for all purposes.For ICTenergy use to ‘only’ doubleover the next decade(as illustratedbelow), hugegains in efficiencywill beneeded –at a time when efficiency gains in ICT have slowed.91ICT willlikely consumetriple the energy of all EVs in the world by 2030(assuminganoptimistic 200 millionEVgoal).92Or,in otherterms, transporting bits now uses 50%more energythanworldaviation, and will likely use twice as muchby 2030

For a smartphone,the embodied energy ranges from 70 to 90% of theelectricity the phonewill use over its life, counting recharging its battery.74,75,76Thus,theenergy use of smartphone itself (i.e., excluding networks and data centers) is totally dominated by manufacturing, not by the efficiency of say thephone’s wall-­‐chargeror battery. This is quite unlike other consumer products.

It takes energy, dominantly electricity, to manufacture ICThardware. Buildingone PC usesabout the same amount of energy as making a refrigerator,for example.67Annualized, theenergy to fabricatea PC is three to fourtimesthat ofa refrigeratorbecause the latter is usedthreeto fourtimes longer

Global traffic on mobile networks is expanding at historically unprecedented rates, rising from today’s 20 to over 150 exabytesa year within a half decade. While today’s networks energy use rangesfrom 1.5 to over 15kWh/GB of traffic,47overall network energy efficiency will need to improve nearly 10-­‐foldin five years to keep total systemenergy use from rising substantially

Reduced to personal terms, although charging up a single tablet or smart phonerequires a negligible amount of electricity, using either to watch an hour of video weeklyconsumes annually moreelectricity in the remote networks thantwonew refrigeratorsuseina year