Weather stations:
Providing business
critical information
As a nation, the U.S. consumes seven percent of the globe’s energy, making it the largest energy
user in the world by a considerable margin.
Weather greatly impacts this number. Building, utility and operations managers use weather
forecasts to make real-time decisions that ultimately add or shed load on the grid. Inaccurate
weather forecasts can increase the amount of energy put on the grid, and in turn increase a
company’s spending for the unneeded production, consumption or generation of energy.
To combat this waste of energy, scalable, local weather stations enable hyper-local, point-based
forecasts that allow companies to make informed, business-critical decisions. These decisions
ultimately help decrease the amount of energy consumed.
Commonly available weather forecasts for a “specific location” are generalized for a larger geographical area. For example, the weather forecast for New York City may be inclusive of Brooklyn, Queens and Manhattan even though they are spread out over a number of miles over varying terrain and experience different temperatures and levels of precipitation at various times.
In fact, these weather forecasts are gathered by national weather stations commonly found at airports, which are often miles outside the city.
Moreover, these forecasts usually combine the weather each city or town might experience throughout an entire day, without any specificity
This information is often ineffective for many weather-dependent businesses, as generalized forecasts lack the precise information they need to make informed decisions. To exemplify just how important weather forecasts are, large power utilities can save more than half a million dollars a day when the temperature forecast accuracy is improved by a mere half a degree.
Figure 1
“General forecast areas” experience a multitude of weather conditions. The image above illustrates two points that are within a “general forecast area,” separated by a distance of less than 10 miles. However, the two points have a 14 degree temperature difference. This is why point-based, hyper-local forecasts are needed to ensure weather forecasts are accurate.
Hyper-local forecasts that can take into account sudden changes in the local weather, and are specific to a single latitude and longitude location, are made possible through local weather stations, such as the Schneider Electric WeatherSentry® Weather Station. Weather stations range in sophistication, accuracy and cost, and the WeatherSentry Weather Station is designed for scalability, making it the ideal choice for any middle- to high-end scope and application.
Hyper-local forecasts
There are a multitude of factors that affect weather at a specific location.
Altitude, topography and foliage are just some of the factors that could differ dramatically between the location using weather forecast information and where the nearest weather station is that is driving that forecast. This can affect temperature, precipitation (including the percent likelihood, type, and amount of precipitation), wind speeds and directions, sunlight exposure, and possibly the occurrence of hazardous weather conditions.
For any professional making weather sensitive decisions, which could range from renewable energy generation forecasting to public safety, these factors are critical. It may not only be the difference in thousands of dollars of expense, but in some cases could save lives.
Hyper-local forecasts are point-based, and are for a specific location and time. These precise forecasts help utility, operations and building managers make more informed, real-time decisions regarding their infrastructure, while also impacting the accuracy of utilities’
energy-demand load forecasting.
Why utilities need precise weather information
Utilities rely heavily on weather forecasts, as weather is the single biggest driver of energy consumption. As a nation, the U.S. consumes seven percent of the globe’s energy, with the majority of that consumption attributing to lighting, heating and cooling. Thus, temperature forecasts are one of the most valuable pieces of information, if not the most, for utilities as they greatly affect electric and natural gas load forecasts.
Sudden, even minuscule, changes in the temperature can severely impact the amount of energy utilities need to generate and provide. In fact, up to 90 percent of errors in load forecasts are the result of a poor weather forecast. Because utilities buy and sell energy days before they actually need it, the price of purchasing energy the day it’s needed comes with a hefty penalty. Utilities need weather stations to provide the most accurate temperature forecast across their service area, not just one territory, allowing them to model and predict exactly how much energy they’ll need to provide to all consumers.
That’s because a utility’s topography and geography can differ greatly across its service area. Service areas often span various elevations, and those in close proximity to lakes and oceans have microclimates.
With different elevations and climates come varying temperatures and precipitation. Hyper-local forecasts driven by local weather stations ensure utilities know what weather will affect a specific point of their service area up to the minute and throughout the entire day, up to 72 hours in advance.
They also can determine if high winds or rain will hit a portion of their service area, while other areas will be hit with ice and snow. This allows utilities to plan for outages before they happen and understand where their pain points will be.
Weather stations also can improve Energy Event Index (EEI) forecasts, which are tailored to a utility’s service area or locations of key infrastructure, and are used to prioritize outage management. An EEI forecast will be generated based on the weather stations in that area, and will calculate risk-factors: Where is the most severe weather going to be? How can utilities prepare for possible power outages? The EEI zones are assigned a one-to-five rating based on the level of weather conditions expected in each area, as well as a color coding index that prioritizes a specific service area for utility management purposes when determining outage response.
These indices are most accurate when used in conjunction with hyper-local forecasts, which in turn are improved with local weather stations.
The importance of
forecast accuracy
Large power utilities can save more than half a million dollars a day when the temperature forecast accuracy is improved by a mere half a degree.
How hyper-local forecasts impact building energy usage
In the U.S., residential and commercial buildings account for 39 percent of energy demand. Up to one third of that usage derives from heating and cooling alone. This number is highly dependent on weather, and could be lowered with accurate, precise forecasts.
Because weather varies all the time, building management systems (BMS) can use weather forecasts to predict the energy needs required to keep buildings at a roughly consistent temperature. The colder the outside air temperature, the more energy it takes to heat a building. Thus, the amount of energy used to heat the building will vary depending on the outside air temperature.
Sophisticated BMS use forecasted temperature data and other factors, such as human occupants and equipment that give off their own heat, to predict the building’s energy needs. BMS can then make informed decisions to construct a strategy to meet those needs, as far as how much energy needs to be created or purchased to meet desired temperatures, using the cheapest energy available.
For example, large universities and hospitals will chill water or make ice at night when energy is cheap, rather than during the day when energy is most expensive. The buildings then use the cold water or ice to keep the buildings cool during the next day. Accurate forecasts will inform a BMS on just how much cold water or ice to create, then enable to BMS to create it when energy is cheap. If there is a short-fall in the amount of chilled water or ice available during the heat of the day, the BMS will have to consume energy to create more ice or chilled water at a time when electrical energy is the most expensive.
Accurate weather data can positively impact the efficiency and
performance of BMS by more than 25 percent. By adding a weather station to the top of a building or parking lot, or attaching it to an object, such as an electrical pole, BMS can get a more sophisticated, accurate view of the weather affecting the building and further improve building efficiency.
Utilizing local solar forecasts
Solar is the most abundant renewable energy source available but the ability to harness and distribute it alongside existing sources of energy is new territory for many utilities. It isn’t easy to control from the perspective of grid operators — who need to balance electricity supply and demand at all times — as not only the amount of power generated is variable based on weather, but also the timing of when the generation will come onto the grid.
With solar energy, there is no “on” or “off” button, and grid operators must accept the solar energy generated.
However, with the right type of weather information, solar power can be more predictable, and therefore less disruptive to the grid.
In the U.S., residential and commercial buildings account for 39 percent of energy demand.
Up to 90 percent of errors in load forecasts are the result of a poor weather forecast.
39% 90%
Scalable, local weather stations
enable hyper-local, point-based
forecasts that allow companies to
make informed decisions.
Solar farms can use one weather station to provide a hyper-local forecast specific to the area to determine when the solar panels will capture energy from the sun. Buildings with solar panel installations also generate energy for their surrounding area with the use of distributed micro-grids. To maintain a stable and efficient grid, it is extremely important for utilities to have accurate solar weather forecasts combined with Advanced Distributed Management Systems (ADMS). An ADMS receives real-time forecasts from weather stations at each distributed micro-grid that it then pulls into its model to predict how much solar power each grid will produce, and when.
An ADMS uses the forecasts to help ensure that the load and supply of power stays balanced. When utilities have to make real-time decisions regarding whether or not to shed load, they can rely on the predictable information derived from ADMS, which collects the real-time output of power from all distributed solar micro grids, and combines that data with all solar forecasts to generate the total output of solar power expected.
Additionally, each micro-grid needs a weather station to provide a hyper-local solar forecast, as it allows grid operators to see the predicted solar power from each micro-grid, and then manage the balance of power most effectively. A local weather station includes one or two solar energy sensors, which when installed alongside a distributed micro grid, gives grid operators a granular view of energy generation due to either or both Global Horizontal Irradiance (GHI) and plane of array (POA) measurements. These allow forecasts to better predict energy generation in that specific area, rather than using a weather station for such predictions that could be as far as 10 miles away, or more.
There is another use for micro-grids, and that is from the facility
management’s point of view. In the above examples, the context was from that of a large grid operator, but companies that invest in installing an energy micro-grid on their facility will want to know they are using it in the most efficient manner. Demand Side Management (DSM) and Demand Side Operations (DSO) systems make real-time decisions on which energy source to use, whether it’s the grid, local solar, wind, local geothermal, etc., what to do with it, whether to power the facility, store it or sell it. These decisions must take into account current and future conditions, and as these systems become more sophisticated, a hyper-local weather forecast is a crucial input parameter.
Ultimately, weather stations provide hyper-local accurate weather information. Even though organizations may install a weather station for a specific reason right now, doesn’t mean it cannot be used for multiple business cases in the future.
Hyper-local forecasts
Hyper-local forecasts are point-based, and are for a specific location and time. These precise forecasts help utility, operations and building managers make more informed, real-time decisions regarding their infrastructure, while also impacting the accuracy of utilities’ energy-demand load forecasting.
Decrease energy consumption
Weather dependent businesses have long relied on generalized, inaccurate weather forecasts to make business critical decisions. This information directly relates to the utility, buildings and solar industries’ decreased bottom lines due to their purchase, generation or consumption of energy that was never needed, or was done so at the least efficient times. This inaccurate information can also be directly attributed to a portion of energy consumption in the U.S. that goes unused.
Reversing this trend, weather stations enable businesses to make smarter decisions when it comes to energy consumption and generation using hyper-local forecasts. No longer will utilities have to purchase extra generation the day it’s needed, as their temperature data will be specific to their service area leaving little room for error. No longer will buildings face a guessing game of temperatures, as weather stations will enable building management systems to understand future weather patterns. Rather than solar energy going to waste, weather stations can predict when and how much energy will come onto the grid at every hour, every day. Demand Side Management systems will allow facility owners to make the most informed decisions regarding the energy its facilities are creating and consuming.
Hyper-local forecasts will help weather dependent businesses and the U.S.
reduce the amount of money spent on generating energy, and ultimately, decrease overall energy consumption.
Now, the question is, how will you use weather stations to offset your business’s wasteful energy usage?
Accurate weather data can positively impact the efficiency and performance of building management systems by more than 25 percent
25%
Utilities rely heavily on weather
forecasts, as weather is the single
biggest driver of energy consumption.
Schneider Electric 9110 West Dodge Road Omaha NE 68114 Tel: 800-610-0777 Fax: 402-255-8125 www.schneider-electric.com
