By Souma Das, Managing Director – India Subcontinent, Alteryx
While a majority of enterprises are realising the ‘green way forward’ to help reduce carbon emissions and improve the business’s “green” credentials, daily ‘energy usage’ can often be overlooked. Larger companies with production facilities, complex supply chains, and set in various locations frequently neglect the expenses of energy used to power these complex operations. Hence, in an attempt to cut back on energy consumption, using strategies like investing in more energy-efficient equipment or turning off the lights at the end of the day is common. But, with rising energy costs pushing many businesses to breaking point, data-driven AI innovations can provide the crucial insights needed to help optimise energy usage.
Throughout the past few years, there have been numerous developments in the energy sector to provide real-time energy usage analysis that can enhance the utilisation of these resources more effectively.
Energy companies are now looking into the possibilities of incorporating data science and AI to increase the prospects of more efficient energy consumption. Several technologies are now being curated to aid energy efficiency. In light of the most recent breakthroughs, AI has revolutionized the fields of robotics, self-driven cars, smart lighting, temperature regulation, etc. In enterprises, large data sets can be compressed and analysed to help monitor and understand the utility requirements to optimise energy use. Here are some ways, AI is helping build an energy efficiency and a sustainable future –
Supply Chain Management
Predictive technology to analyse movement in the production, movement and sales through forecasting demand and supply plays a key role in managing supply chains. By analysing manufacturing and inventory data, AI can predict when a supplier will run out of a given product and the re-production requirements. AI can also provide insight into ineffective scheduling, planning, and the creation and administration of an intelligent warehouse with lower overall expenses.
Catering to the consumers, the buyer can be given the choice of altering their order or waiting until they are automatically informed of the projected restocking date. On the other hand, AI can forecast anticipated product shortages or price changes, allowing consumers to store up.
Large companies find a severe need to track transportation in terms of passengers and freight. While most other aspects of a company can be effectively introduced to smart energy saving systems, transportation management requires a focus on various factors like fuel efficiency, occupancy, distance optimisation, nature of the vehicle, etc. AI can help companies derive the most out of transportation through –
• Route optimization
• Ensuring complete loads on both the outbound and return trips
• Notifying business partners in advance of delays
• Automating arrival notifications to cut down on drop-off wait times
• Scheduling maintenance and repairs
• Preserving fuel economy
Building and factory energy
While large infrastructure means greater energy use, it is possible to bring it under margin and beneficially use energy sources. AI can support in combining utility bills and examining energy usage that can help derive insights on energy requirements and consumption. Therefore, it will be possible to pinpoint operations that use a lot of energy and identify the peak load times. In a smarter manner, moving those operations to off-peak times and scheduling power cuts at non-work hours can amount to a larger savings on energy use and budget.
While modern power systems consist of several sources of energy (like solar, wind and coal), smart grid management systems help schedule and track energy usage as per requirements. Companies must take into account the unpredictable nature of renewable energy sources like solar and wind if the energy strategy calls for them. AI can be used in creative grid management to aid in energy predictions and storage. Incorporating weather forecasting and analysing available resources can help allocate where energy comes from, how much is utilized and how it can be saved. Operating large grid structures is complicated and through the involvement of AI, it is possible to reduce the manual workload and effectively manage the power systems.
Data science and AI are inextricably intertwined, providing innovations that help in accident management, energy forecasting and energy storage a to transform the way enterprises approach energy efficiency. An improvement in energy efficiency doesn’t really appear to be too far off with the involvement of AI in the energy sector. But to be effective, AI-driven decision intelligence must be underpinned by a strong foundation of data literacy, as well as proper training and upskilling. Only through the combination of quality data, diverse human intelligence and a robust governance process, will AI become the force behind automated business decision intelligence capable of improving energy efficiency.