The use of data as a research tool is widespread in academia and industry. In many ways, we are already reliant on data. To name just a few examples: the majority of traffic lights now use data to control their green lights, the internet uses data to route our packets, and the UK National Health Service uses data to monitor the progress of patients and doctors alike. Data is a powerful tool, but it comes at a cost. Many of our data-driven services require a large infrastructure, which requires a lot of electricity – so why not use clean energy?
There are a number of ways that researchers are improving our understanding of the green technologies available, how these can be used, and ultimately how to reduce the carbon emissions generated through the energy production process. Researchers at University College London recently published a study which analyzed the electricity demand profiles from 10,000 households across Europe. The researchers were able to develop algorithms to estimate the amount of power consumed in each house. The findings are particularly useful as a baseline reference point for comparing different energy options, and also to provide an accurate indicator of the amount of energy that could potentially be saved through the adoption of new energy technologies.
The development of renewable energy is a crucial part of efforts to tackle climate change, and the data available to researchers such as those at UCL, can be used to provide evidence to policy makers and the public alike. For example, a recent report produced by the Department of Energy and Climate Change (DECC) concluded that there was a significant potential to increase the penetration of solar PV, and hence reduce the amount of CO2 emitted. However, DECC found that the available data was inadequate to quantify this potential. As a result, the authors were unable to accurately predict the size of the market, or to identify the barriers to increasing uptake.
This problem is being addressed through collaboration between industry and academics. A number of organizations, including the British Solar Trade Association, the Institution of Engineering and Technology, and the Renewable Energy Association, are working together to produce a common dataset on solar photovoltaic (PV) systems, to help researchers better understand the market potential of the technology.
For other researchers, the data is not always available. While it is possible to use household surveys to capture information on household consumption patterns, this method has several limitations. Firstly, it can be difficult to capture the nuances of the behavior associated with different technologies, such as Delphix.
For example, if you ask a household whether they would consider installing a solar PV system, you will get a ‘yes’ or ‘no’ answer, but you won’t get the details of why they choose one over another. If you instead asked people directly why they selected a particular technology, you would get a more accurate reflection of the actual choices being made. Secondly, even if you do gather this kind of detailed data, it does not provide the information needed to identify the full range of options that are available.
The use of data to improve our understanding of energy technologies is not limited to renewables. The ability to track how a technology performs is also vital for the deployment of nuclear reactors. This means that researchers have been using sensors in order to measure the performance of nuclear reactors, and thereby better understand their operation. A recent publication by researchers at the National Nuclear Laboratory and the Institute for Energy Technology provided a detailed analysis of the performance of a reactor at the Dounreay site in Scotland.
By measuring how the temperature and pressure inside the reactor changed as a function of time, it was possible to model the core’s thermal and mechanical behavior. This led to the development of algorithms which can be used to estimate the reactor’s lifetime, and also provided valuable insight into the processes that occur inside the reactor and how they affect its performance.
How scientist use data in green energy
Data is the key to unlocking many of today’s problems and issues. Scientists use this data to help create solutions and ways of tackling these. It’s why they need to gather data, so they can find out how to produce the most sustainable and efficient way of producing electricity.
Many scientists today use advanced equipment to look at data. They are analyzing how the earth’s climate is changing and what it will mean in the future. They have created ways of calculating how much carbon dioxide will remain in the atmosphere. This allows them to forecast what will happen and make decisions based on this.
There are many factors that affect the world. Some scientists are looking at renewable energies, such as solar and wind power. These have many advantages, such as creating jobs and making countries energy independent. They can be cheaper than oil, and can provide the majority of the worlds’ energy needs in many cases.
There are many types of renewable energy but the best known is wind power. Wind turbines have been around for a long time. They were used in places like Ireland, Denmark and Norway. The technology has moved on a lot since then. Today wind turbines can provide 10% of the worlds’ electricity needs. The industry is worth billions of pounds to many countries.
Solar power is another type of renewable energy. Solar panels collect energy from the sun and use it to create electricity. This type of renewable energy is growing quickly and it’s already contributing to some countries energy supply.
Scientists are looking into the use of hydrogen and the potential to create renewable energy. A form of hydrogen called water fuel cells are used in cars and are one of the biggest areas of interest. The process of putting hydrogen in a car works the same as that of a traditional fuel cell, but it’s cleaner, greener and easier. Hydrogen can be produced from biomass (plants/organic matter) and water.
In summary, we are already dependent on data for a huge number of things, but this dependence will only increase. If we want to reduce the environmental impact of our energy use, then understanding the environmental performance of the technologies we adopt is a critical component of achieving this goal. Using data science in renewable energy, we can quantify the amount of energy being generated by an individual green energy technology.