Big Data Processing for Commercial Buildings and Assessing Flexibility in the Context of Citizen Energy Communities

Abstract

In this paper, we propose a cloud-based big data processing approach to evaluate the flexibility potential of commercial buildings by type and benefits for the owners. The pandemic times changed electricity consumption patterns with a substantial impact on energy markets. Many activities moved from large commercial offices and schools to residential buildings. With machine learning algorithms, the flexibility forecast can be improved to help energy suppliers, grid operators, and traders better calculate the flexibility potential of commercial buildings. With better forecasts, grid operators can identify and mitigate risks, prevent malfunctions, and schedule maintenance works in advance. Using flexibility forecast as input and results from previous studies regarding flexibility coefficient by state and demand response programs, we propose an original method to assess load flexibility of commercial buildings and calculate the benefits for their owner. The exemplification is done with an extensive hourly dataset from the U.S.A. of 14,976 comma-separated values files with a total of 131.18 million records showcasing the electricity and gas consumptions and their breakdown for one year.

Keywords

• Electricity
• load management
• power generation economics
• clouds
• databases
• environmental economics