How Smart Meter Data Can Mitigate Climate Change In The Energy Sector
It’s not just the effects of plastic and other materials to our environment that we should be concerned with. Right at this moment, the demand for energy globally is increasing and with this, greenhouse gas (GHG) emissions from the energy industry is also at a rapid rise.
Unfortunately, this trend isn’t stopping soon due to economic growth and a population that keeps swelling. As climate change worsens, more challenges for energy production and transmission materialize. We’ll see an increase in the severity and frequency of extreme weather events which will affect energy production and delivery.
With the help of data, technologies and software are now readily available to help companies to acquire intelligence where they need to make adjustments that will impact their climate targets. It can also aid in their operations, such as preparing for storm surges by accurately predicting weather conditions. Coincidentally, these advantages extend to the energy sector. By gathering and analyzing data, this industry can help mitigate climate change by finding solutions to reduce energy consumption, especially for households that are being hit hard by the effects of the current energy crisis. However, tracking energy consumption in households involves more than just looking at your power bills.
With a powerful analytics platform that can collect smart meter data from all households as well as historical data, the grid, the weather, and other sources, businesses and customers can understand in detail just how much energy different spaces and systems consume.
Moreover, everything gathered from here can be transformed into actionable insights that organizations can use to find areas where they can effectively reduce energy consumption.
Identifying Peak Demand Periods
Long gone are the days of guessing peak demand times. With smart meter data, organizations can determine this with incredible accuracy.
Analytics platforms can use data from meters to build accurate forecasting models to obtain predictions and estimations of how much energy consumption might be being used for a specific group of customers in a designated area or on a specific date. Energy companies can also utilize these platforms to help their customers reduce usage by taking proactive measures to reduce the load.
Let's illustrate this. For instance, an analysis into the consumption data of a four-member family household shows that the family uses more electricity in the afternoon when the individuals return from work and afterschool activities. Data also identified that the family shares an electric vehicle (EV). Once the data analysis has detected this type of consumption, personalized suggestions can be made to the family in order to shift to a contract that has lower rates for afternoon electricity or times of the day when the EV can be charged at a lower cost.
Recognizing Equipment & Infrastructure Problems
While smart meter data may be available, energy consumption can still be high—and the reason may not be immediately apparent. For instance, energy use may considerably rise if some home appliances are very old or energy inefficient. While data on energy use can demonstrate this rise in demand, pinpointing the precise home appliance and its inefficiency might require some investigation.
With IoT and analytics, organizations can easily detect energy guzzlers. This will then alert customers to performance and energy consumption irregularities that must be investigated or replaced. Finding these anomalies reveals instances where efficiency can be increased through equipment maintenance, repair, or replacement.
Adopting Renewable Energy
Today, several renewable energy solutions are not just more advanced than they were 10 to 15 years ago, they are also more affordable. For example, solar energy can be harvested anywhere there is sunlight and open spaces. However, adopting renewable energy sources presents some difficulties since they are unpredictable and highly variable in various parts of the world. Technologically-sophisticated analytics can assist renewable energy companies in gaining important insights to aid in better managing wind and solar. For example, they will be able to predict more accurately the amount of energy that can be used in the power grid or saved for later use.
Creating Early Warnings
One of the biggest concerns with adopting renewable energy is the lack of consistency due to shifting weather patterns and the possibility of energy waste when not needed. To match the reliability of fossil fuels, data management systems can make predictions and forecasts for the energy demand based on weather patterns, daily energy usage patterns, and the time of day.
Building A Better World With Smart Meter Data
Having more data leads to better insights! Over the years, data analytics, data science, and machine learning have matured and continue to improve upon existing data models and algorithms. And with the rate at which it is going, mitigating climate change could be possible in the energy sector.
With data collected and analyzed, we can improve energy use to achieve energy efficiency and push toward a more sustainable future. For more insights on the energy sector, power up your mailbox with NET2GRID.