Energy powers industry, businesses and day-to-day lives. With the introduction of application sensors, wireless transmission, network communication and cloud computing, the energy sector is booming with a constant influx of valuable data. Data analytics tools provide efficient energy analytics to harness the nuances of the data produced by various energy sectors and employ it more beneficially.
The big data analytics market in the energy sector is expected to grow at a CAGR of 10. 22% during the forecast period 2020-2025. This boom has put energy analytics at the forefront of sustainable and efficient decision-making. Energy analytics is the process of gathering data using analytics tools to assist energy suppliers in analysing, supervising, and optimising energy-related KPIs, like production costs, consumption, production and distribution.
Read on to see how innovative data analytics tools are revolutionising our energy market.
Smart metering in big data analytics involves components, such as grid operations, field services, resource planning, customer experience and regulatory compliance. It helps to predict demand based on the data collected, which is likely to boost energy market growth. Moreover, big data analytics in smart metering helps forecast energy consumption, which plays a vital role in the management of demand and supply, mitigating energy waste.
For instance, some companies incorporate data analytics tools with smart meters to achieve deeper insights from the smart grid, which, in turn, offers information to mitigate extra energy consumption and increase profit margins.
Data analytics tools play a crucial role in reducing energy consumption, improving efficiency and exploring alternative energy sources.
The volatility in oil prices leads to high expenditure in energy-related projects, which, in turn, creates demand for big data analytics. As interest in cleaner energy grows, dependency on alternative energy sources, such as solar power, wave energy and wind turbines will increase.
In the smart grid, wind and solar power are two major renewable energy power generation methods. Yet, weather conditions significantly affect their output. By using data analytics tools, renewable energy power generation forecasting will be more accurate and efficient.
Temperature, atmospheric pressure, humidity, cloud cover, wind speed and wind direction are all data points that can support the site’s selection of renewable power generation devices. It includes geographical information from satellite imagery that aids spatial planning, analysing topography, locating water sources and solar irradiation, improving power output and energy efficiency.
Data is not just gathered from producers and consumers; it is also created by the grid infrastructure used to transmit it. Analysis of these grid data points allows utilities and energy companies to identify which assets are in most urgent need of repair or replacement.
By creating predictive algorithms based on historical and current data and combining them with risk models to rank risk levels across different geospatial locations, predictive analytics takes operational risk management well beyond traditional methods. This is vital for ensuring energy networks can function at full capacity. It can also reduce supply disruption and outages because of risk modelling of factors, like transformer overheating, leakages, disruptions, dramatic usage spikes or even trees falling on power lines.
Big data analytics tools help the energy utility sector by optimising power generation and planning. Power generation planning and economic load dispatch are the two most important decision-making processes in power generation. Economic load dispatch, in simple terms, is matching power supply with the energy demand, over a short period of time, at the lowest possible cost, subject to transmission and distribution constraints.
Matching energy supply and demand in the network has always been a tight balancing act, and data analytics plays a significant role by using energy data and advanced analytics techniques to maintain an optimum level, avoiding massive large energy leakages.
The energy sector is undergoing radical changes at a rapid rate, moving towards more decentralized power generation, intelligent distribution grids, the capacity to instantly buy/sell, smart meters and innovative customer service. For these efforts to be effective, long-lasting and successful, it requires data analytics tools to provide actionable insights to the energy sector.
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