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  • Aishwarya Jankar

Machine Learning in Power Industry

According to a new market intelligence report by BIS Research titled 'Global Artificial Intelligence (AI) in Energy Market – Analysis and Forecast, 2019-2024', the artificial intelligence in energy market is expected to reach $7.78 billion by 2024. The market is projected to witness a CAGR of 22.49% from 2019 to 2024.


From analyzing huge amounts of data to computing insights through the accounting and auditing systems, AI has proven to be of major assistance to the energy industry. Machine learning facilitates training algorithms on big data and enables structure, storage and ease of operations. At Algo8 ,we help in revamping the energy grid to a smart grid by providing proven insights on forecasting of demand, production and power price.


The energy sector of India has faced consistent problems ranging from technical efficiency to electricity theft. By the means of technical advancements, they were able to schedule, dispatch and use advance algorithms for smooth functioning. Recognition of squandering electricity is quite difficult to spot in this huge electricity consumption database. AI algorithms are used to avoid any flaws or damaging effects that may lead us to big fiscal losses. Some of these are basic functions by AI that improve productivity and enhance the process: surveillance, examine electricity consumption at frequent intervals and control current and future damages.


Source: SITN, Harvard University


Forecasting demand and consumption of energy is one of the most important roles of machine learning models. Bifurcation of feeders according to Voltage regulation can be monitored through such systems and installing additional switches between feeders will enable appropriate load transfer to the respective units. This valuable data is used by growth and facility managers and power facilities to install energy reduction policies. Machine learning algorithms can configure the energy requirement on any particular day based on the daily consumption variations that take place according to the data of an individual gained over a period of time. Optimization of operations and resources will be simpler through such forecasts that will lead to the sustainable growth of the organization along with thereduction in waste of energy.

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