The Evolution Of Machine Learning In The Energy Industry
The confluence of machine learningenergy industry will revolutionize general living standards in the very near futureThe synergy of artificial intelligencerenewable energy will have far-reaching consequences on the economy, environmentour lives.
Machine Learning Is The Future
There is hardly any field that will be left untouched by machine learningAIIn fact, machine learningAI are already transforming all possible sectors that you can think ofMachine learning together with the Internet of Things is interconnecting everything into one smart networkThese technologiestheir rapid developments hold many far-reaching implications for energy.
Machine learning is ushering in what is being described as the 4th industrial revolutionWhile the previous industrial revolutions were orchestrated by steam, mass productionthen computers, the next impending industrial revolution will be brought about by AImachine learningThe energy sector in particular will become more efficientrobust as a result of machine learning.
Machine learningAI can help to reduce electricity wastage, create accurate forecasts for demandmitigate the susceptibility of the grid to blackoutsMachine learningrenewable energy will mean more efficientreliable electricity production, as well as greater empowerment among those on the supplydemand side.
Machine Learning For More Consumer Power
One important development in the energy sector is increased diversity in electricity generationSince electricity generationdistribution requires an enormous investment on infrastructureequipment, the energy sector is essentially an oligopoly due to the relatively few companies involvedHowever, renewable energy has purveyed several benefits, not the least of which is that homeowners in certain regions can now generate their own electricityeven sell it to energy companies.
However, with so many entities supplyingconsuming electricity simultaneously, there are certain challengestechnicalities that must be overcome to ensure seamless grid operationThis will be made possible by machine learning since it will soon be able to forecast with a high degree of accuracy, the movement of demandsupplyDistributed generation of electricityincreased consumer power will follow when machine learning boosts the viability of renewable energy generationThat could happen sooner than you think.
Machine Learning For Higher Efficiency And Less Waste
Another important problem arises when there is a greater gap between demandsupplyWhen electricity demand outstrips supply,renewable resources can not meet the excess demand, then electricity companies have to activate backup unitsThis entails high costs since it involves much inefficiencyBut with the advent of machine learning, companies will be able to forecast excess demand accurately to take timely actionthus save demand change costsA cumulative effect could be consumers benefiting from lower energy billsThe usage of redundancies such as fossil fuels could also be reducedoptimisedas a result lead to fewer carbon emissionsMachine learning for the energy sector is the need of the hour since the gap between demandsupply will widen as energy requirements, for the likes of smart devicessystems, big data processing, real estateother applications, increase in the future.
The Department Of Energy And The Smart Grid
To this end, global infrastructuresinitiatives are integratingplanning for improved AImachine learning capabilitiesFor example, the Department of Energy (‘DOE’) stated back in 2010 that it will createimplement the smart gridThe DOE has proposed that the smart grid will enable the seamless two-way flow of electricity for all usersIn other words, consumers will be able to consume as well as supply electricity (from renewable resources) without problems thanks to the smart gridThe smart grid will also facilitate the flow of information.
A major advantage of this system will be that the national grid will become more robustThe current grid is surprisingly susceptible to surges in electricity consumptionThis became painfully obvious back in 2003 when a huge blackout ensued after a part of the grid broke down under excessive loadThis created a cascade effect that left huge swathes of the country without electricity for days thereby incurring billions in damagesA technological solution is needed to mitigate this susceptibilityThe smart grid, powered by AImachine learning is the answer.
The US DOE has already invested over $4.5 billion on the smart grid infrastructure with more than 15 million smart meters installedThe department has estimated that electricity demand will rise by 25% towards 2050However, with the smart grid in place, the peak electricity load will increase by only 1 percentage pointMachine learningAI will be directly responsible for the increased efficiencyflexibility of the futuristic grid.
Intelligent AI-Driven Infrastructure
The smart grid of the future will be powered by intelligent devicesmeters that will transmit huge amounts of data concerning electricity consumption patternsThese devicesmeters will be driven by machine learningAIAll the information collected from these sources will then be analyzed by machine learningAI algorithmsData processinganalysis will help uncover patterns in electricity consumption to forecast electricity demand with a high level of reliability.
Smart electricity meters will orchestrate a revolution in electricity supplyThese sensors can relay electricity consumption datapatterns so that real-time monitoring of the demand/supply equation becomes possibleInformation provided thus will help electricity companies to avoid disruptionsblackouts that come with sudden surges in demandThe result will be a more reliable supplythe mitigation of blackout risksSmart systems will also be able to better managebalance electricity demand with supplyThe key benefit will be a substantial cut in inefficiencywastage so that consumers have to pay lower billsMajor organizations like Google have already implemented these technologies to slash electricity costs for their data centers.
Machine learningAI systems can help to reduce the reliance on fossil fuelsThere will be greater room for renewable energy sources owing to increased efficiencies in electricity transmissionOne of the biggest problems with renewable energy is that the supply is subject to weather condition variationsFor instance, solar energy is affected by cloudy weather while wind energy is affected by calm weatherMachine learning can help to predict these shortfalls in supply with accuracy so that renewable energy becomes more predictable.
As leading energy consultants in the global field, Pangea Strategic Intelligence can help corporationsenergy investors understand where they should investhow they can capitalise on these exciting developments.
