In 2018, the number of Australian households with rooftop solar passed 2 million – that's one in five.¹ Tomorrow's smart grid will be a constellation of many generation sources working together, shifting from the traditional one-way power flows from generation through grids to consumers to two-way flows including from the customers back into the grid. As we move towards decentralisation, there is an urgent need for new business models and the technology to support it. A new wave of innovative technologies such as Internet of Things (IoT), Edge and Fog computing, blockchain, machine learning and Artificial Intelligence (AI) will become key enablers for such a transformation. Cisco in partnership with the University of Technology Sydney (UTS) and SAS embarked on a trial where the feasibility and economic benefits of DER aggregation and a real-time energy brokerage in a residential framework were successfully designed, tested and verified.
Argonne researchers are using artificial intelligence to speed up the day-ahead electricity market clearing and real-time operations. The electricity market clearing and grid operations rely on the security constrained unit commitment, or SCUC, which helps grid operators set a schedule for daily and hourly power generation. As the SCUC problem is solved multiple times a day, data accumulates that can be used to discover patterns applicable to solving the next round of problems. To that end, Argonne researchers have developed AI that now can solve a SCUC about 12 times faster than conventional methods. Researchers continue to refine the method, an early version of which was used successfully in tests at Midcontinent Independent System Operator (MISO), overseeing electricity market and delivery across 15 states and one Canadian province.
"Time series models are used to forecast future events based on previous events that have been observed (and data collected) at regular time intervals." We will be taking a small forecasting problem and try to solve it till the end learning time series forecasting alongside. Time series forecasting is a technique for the prediction of events through a sequence of time. The technique is used across many fields of study, from geology to behavior to economics. The techniques predict future events by analyzing the trends of the past, on the assumption that future trends will hold similar to historical trends.
The term Deep Reinforcement Learning is a new cool phrase in the world of Artificial Intelligence and Machine Learning. So, what does this phrase mean, and what is its impact? Deep Reinforcement Learning uses the combined principles of deep learning and reinforcement learning. Deep Learning, as we know, Deep learning is a part of machine learning methods and is based on artificial neural networks. Reinforcement Learning, on the other hand, is an area of machine learning which tells how software agents should take actions to maximize the probability of choosing the best possible path or behavior for a particular situation.
The automated nature of such attacks means that they can be launched at speeds far in excess of what humans are capable of, he said, suggesting that attacks could happen on a microsecond-by-microsecond level. "We're going to have to understand the implications of, not people-to-machine attacks, but machine-to-machine attacks," said Mr. Fanning. Some security teams are using AI defensively, but cybersecurity leaders across sectors worry that the same technology could propel sophisticated attacks that will be difficult to fend off. A congressional report published last year raised the possibility of AI-based attacks overwhelming grid defenses. Utilities need to invest in defenses and do so quickly, said Mark James, an adjunct professor of law at Vermont Law School and a co-author of a report on state power utilities' cybersecurity practices, published this month.
Utilities supplying electricity in urban areas tend to get inundated by customer complaints of power outage, low voltage and other issues during summers and monsoon. Telephone helplines manned by humans tend to be inaccessible as small groups of workers who man the helplines cannot respond to the flood of calls. The state-run Bengaluru Electricity Supply Company (Bescom), which supplies power to eight districts in Karnataka, has now turned to an artificial intelligence-powered system to service the over 9,000 complaints it receives on its helpline systems each day. Bescom is tying up with the Medical Intelligence and Language Engineering (or MILE) Lab at the Indian Institute of Science (IISc) to create an AI-based complaint response system than can cater to as many as 500 calls at a time -- much higher than the 60 under the system that the utility employs at present. "What we will have is a technology where when someone calls in the machine will take over and the machine will recognise whatever the speaker says whether it is in Kannada or English. It will understand the complaint -- whether it is with regard to a bill or power outage or concession for solar energy -- and find an answer from the server. It will then synthesise the answer again to text and convert to speech," said A G Ramakrishnan, the head of IISc's MILE.
Editor's Note: This is the first in a four-part series examining the growing role of machine learning and artificial intelligence in the power sector. Tomorrow, we look at how regional grid operators are using AI to optimize operations. The future of the electric grid is undoubtedly cleaner and more efficient and distributed, with hefty doses of technology and machine learning helping to operate it all. But if you're expecting a system dramatically transformed, experts say you'll be left waiting. Artificial intelligence and machine learning are already helping utilities run their networks more efficiently, extending the life of equipment and helping to dispatch energy into markets more efficiently.
A major wildfire spread through Colorado, and I spent long hours locating shelters, identifying evacuation routes and piecing together satellite imagery. As the Fourmile Canyon Fire devastated areas to the west of Boulder, ultimately destroying 169 homes and causing $217 million in damage, my biggest concerns were ensuring that people could safely evacuate and first responders had the best chance of keeping the fire at bay. I spent it sitting comfortably in my home in Bloomington, Indiana, a thousand miles away from the action. I was a volunteer, trying to help fire victims. I had created a webpage to aggregate data about the fire, including the location of shelters and the latest predictions of fire spread.
Man-made brainpower (AI) will soon be at the core of each major technological framework on the planet to manage and get to your strategic information. Only a couple of uses are cyber and homeland security, anti-money laundering, payments, financial markets, biotech, healthcare, marketing, natural language processing (NLP), computer vision, electrical grids, nuclear power plants, air traffic control, and Internet of Things (IoT). Artificial Intelligence is turning into a significant staple of innovation, scarcely any individuals comprehend the advantages and weaknesses of AI and Machine Learning innovations. While machine intelligence is sure to assume a key role in the making of cutting edge frameworks in a wide assortment of industry areas sooner rather than later, it is especially applicable in quickly developing businesses, for example, ICT, manufacturing and transportation. Over the globe, mobile operators are preparing to deploy the fifth era of 3GPP mobile wireless networks (5G).
"Digital transformation has become the mainstay for all businesses today and one of the drivers of this revolution is Artificial Intelligence (AI). There is no denying that AI is destroying seemingly insurmountable business barriers at an all astounding rate. Today, AI is instrumental in transforming the way all industries work – from dynamic manufacturing, healthcare industries or the rapidly evolving automotive and power sector," says Yeshraj Singh, Strategic Initiatives Leader – Digital Transformation, QuEST Global in an article published by Energetica India.