Thanks to the often dramatic swings in the price of oil, natural gas, electricity and other energy sources, many companies often find themselves reacting to market prices and paying stiff costs for energy.
In fact, the spot price for West Texas Intermediate Crude Oil has skyrocketed more than fivefold between 2002 and 2012, according to a report by The World Bank.
Under the terms of a 10-year agreement, Google will purchase the entire electricity output of a new wind farm being built in Maevaara in northern Sweden. The electricity will be pumped into the grid and be used to power Google’s massive data center in Finland.
Most companies don’t have the buying power to purchase the entire energy output from a power producer like Google. But there are ways that industrial and commercial energy consumers can leverage data analysis to identify opportunities to protect their interests and guard against market fluctuations in energy prices.
For instance, procurement managers and other corporate decision makers can use analytics to develop cost models that can be used to analyze different scenarios for energy sourcing alternatives as energy prices oscillate.
Meanwhile, companies with operations located in US states where electricity and/or natural gas are deregulated and open to competition such as California, Illinois, and Florida can apply market data and analytics to explore different options for energy to reduce their exposure to price risk, according to Allied Power Services.
Predictive analytics can further help companies sift through historical and real-time data to foresee potential spikes in energy prices. Additionally, predictive analytics can help firms anticipate changes in their organizations’ near-term or long-term energy consumption based on a variety of factors, including changes in production cycles, planned divestitures or office/plant expansion, etc.
Armed with these insights, executives can be better positioned to negotiate pricing for short- and long-term energy contracts, depending on their organizational requirements.
As a growing number of organizations such as Google turn to renewable energy to power their facilities, companies can also use data analysis to identify and mitigate against risk exposures associated with renewable energy projects.
These include potential issues that may affect financing as well as weather-related event and system design issues that can be applied to help diminish risk and improve the potential for higher rates of returns on renewable energy projects.
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