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| Steam rises from a smoke stack at the Big Bend Power Station coal-fired power plant in Apollo Beach, Fla., on January 7, 2017. As climate change increases temperatures, more people will rely on air conditioning, increasing energy demands. Photo by Kevin Dietsch/UPI | License Photo |
By Brooks Hays, UPI
Energy providers are underestimating the long-term effects of climate change on electricity demands, according to new research.
In a new paper, published Tuesday in the journal Risk Analysis, scientists from the University at Buffalo and Purdue University argue current energy demand models are unreliable and imprecise.
The researchers developed a better way to predict future energy demands using a pair of more accurate predictors -- mean dew point temperature and extreme maximum temperature.
"Existing energy demand models haven't kept pace with our increasing knowledge of how the climate is changing," Sayanti Mukherjee, assistant professor of industrial and systems engineering at Buffalo, said in a news release. "This is troublesome because it could lead to supply inadequacy risks that cause more power outages, which can affect everything from national security and the digital economy to public health and the environment."
As global temperatures continue to rise, more people are likely to rely on air conditioning to keep cool. As a result, electricity demands are expected to increase.
But how much will electricity demand increase? The answer depends on the model.
One common energy demand model, called MARKAL, fails to account for climate variability, authors of the study argue. Another, the National Energy Modeling System, or NEMS, does consider climate, but according to Mukherjee and her research partners, its methodology is imprecise.
The NEMS model predicts heating and cooling days by adding the day's high and low temperature and dividing by two. However, the method fails to account for time.
If the thermometer reads in the low 80s for most of the day, but a cold front moves in during the evening, bringing a low of 60 with it, the mean temperature gives the impression of a mild day.
Scientists at Buffalo and Purdue surveyed a full range of weather indicators to determine which metrics are the most accurate predictors of energy demands. They found dew point temperature -- the temperature at which air is saturated with water vapor -- and the daily high temperature were the best predictors of energy demand.
To build a more accurate energy demand prediction model, researchers combined the new climate prediction factors with energy, weather data and socioeconomic data. When researchers used the new model to predict future energy demands across the state of Ohio, they found the energy demand by the residential sector is more directly affected by climate variability.
The new model showed a moderate increase in the average dew point temperature could boost energy demand by 20 percent in the residential sector and 14 percent in the industrial sector.
"The availability of public data in the energy sector, combined with advances in algorithmic modeling, has enabled us to go beyond existing approaches that often exhibit poor predictive performance. As a result, we're able to better characterize the nexus between energy demand and climate change, and assess future supply inadequacy risks," said Roshanak Nateghi, assistant professor of industrial and environmental engineering at Purdue.
Fossil fuels still supply nearly two-thirds of the United States' electricity. Unless more coal and natural gas plants are phased out in favor of renewables, an increased demand for electricity is likely to increase carbon emissions.
Energy providers are underestimating the long-term effects of climate change on electricity demands, according to new research.
In a new paper, published Tuesday in the journal Risk Analysis, scientists from the University at Buffalo and Purdue University argue current energy demand models are unreliable and imprecise.
The researchers developed a better way to predict future energy demands using a pair of more accurate predictors -- mean dew point temperature and extreme maximum temperature.
"Existing energy demand models haven't kept pace with our increasing knowledge of how the climate is changing," Sayanti Mukherjee, assistant professor of industrial and systems engineering at Buffalo, said in a news release. "This is troublesome because it could lead to supply inadequacy risks that cause more power outages, which can affect everything from national security and the digital economy to public health and the environment."
As global temperatures continue to rise, more people are likely to rely on air conditioning to keep cool. As a result, electricity demands are expected to increase.
But how much will electricity demand increase? The answer depends on the model.
One common energy demand model, called MARKAL, fails to account for climate variability, authors of the study argue. Another, the National Energy Modeling System, or NEMS, does consider climate, but according to Mukherjee and her research partners, its methodology is imprecise.
The NEMS model predicts heating and cooling days by adding the day's high and low temperature and dividing by two. However, the method fails to account for time.
If the thermometer reads in the low 80s for most of the day, but a cold front moves in during the evening, bringing a low of 60 with it, the mean temperature gives the impression of a mild day.
Scientists at Buffalo and Purdue surveyed a full range of weather indicators to determine which metrics are the most accurate predictors of energy demands. They found dew point temperature -- the temperature at which air is saturated with water vapor -- and the daily high temperature were the best predictors of energy demand.
To build a more accurate energy demand prediction model, researchers combined the new climate prediction factors with energy, weather data and socioeconomic data. When researchers used the new model to predict future energy demands across the state of Ohio, they found the energy demand by the residential sector is more directly affected by climate variability.
The new model showed a moderate increase in the average dew point temperature could boost energy demand by 20 percent in the residential sector and 14 percent in the industrial sector.
"The availability of public data in the energy sector, combined with advances in algorithmic modeling, has enabled us to go beyond existing approaches that often exhibit poor predictive performance. As a result, we're able to better characterize the nexus between energy demand and climate change, and assess future supply inadequacy risks," said Roshanak Nateghi, assistant professor of industrial and environmental engineering at Purdue.
Fossil fuels still supply nearly two-thirds of the United States' electricity. Unless more coal and natural gas plants are phased out in favor of renewables, an increased demand for electricity is likely to increase carbon emissions.
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