#ai machine learnig

[predicting-energy-consumption]

Predicting Energy Consumption Using Machine Learning in Israel

Machine learning, a subset of artificial intelligence, has revolutionized various industries by enabling advanced data analysis and prediction capabilities. One sector that greatly benefits from this technology is energy consumption forecasting. In Israel, where energy efficiency and sustainability are paramount, machine learning is playing a significant role in predicting and managing energy consumption. This article explores how machine learning is transforming the energy landscape in Israel, empowering decision-makers, and fostering a sustainable future.

Individual Energy Consumption Optimization

At the individual level, machine learning algorithms can analyze household data such as weather conditions, occupancy patterns, and appliance usage to predict energy consumption accurately. This information can be used to optimize energy usage, minimize wastage, and reduce electricity bills. By implementing smart meters and IoT devices, Israeli households can gather real-time data, which is then fed into machine learning models to provide personalized energy consumption forecasts and recommendations.

Planning for Energy Demand at a Larger Scale

On a larger scale, machine learning algorithms are being utilized to predict energy consumption trends for cities, regions, and even the entire country. These models take into account factors such as population growth, economic indicators, weather patterns, and infrastructure development to forecast energy demands accurately. This enables energy companies and policymakers to plan ahead, ensure grid stability, and make strategic investments in renewable energy sources.

Load Forecasting for Grid Stability

Furthermore, machine learning algorithms can aid in load forecasting, which is crucial for balancing energy supply and demand. By accurately predicting peak loads and consumption patterns, power grid operators can optimize electricity generation and distribution, thereby reducing the risk of blackouts and improving overall grid efficiency. This is particularly important for Israel, where demand for electricity fluctuates due to factors like weather conditions and religious holidays.

Integrating Renewable Energy Sources

Another significant application of machine learning in energy consumption prediction is in the field of renewable energy integration. Israel has been actively investing in solar and wind energy projects to reduce its dependency on fossil fuels. Machine learning models can analyze solar radiation, wind patterns, and historical production data to predict renewable energy generation accurately. This information helps in effective integration of renewables into the existing energy infrastructure, ensuring a smooth and reliable transition to a cleaner energy mix.

Ensuring a Greener Future

In conclusion, machine learning is revolutionizing energy consumption prediction in Israel. By harnessing the power of data analysis and predictive algorithms, decision-makers can optimize energy usage, plan for the future, and promote sustainability. Whether it's at the individual household level or on a national scale, machine learning enables accurate forecasting, load management, and integration of renewable energy sources. As Israel continues to lead in innovation and sustainability, machine learning will remain a vital tool in shaping the country's energy landscape and ensuring a greener future.

As technology continues to advance and more data becomes available, machine learning algorithms will become even more sophisticated, leading to improved energy consumption predictions and increased efficiency in energy management. By embracing these advancements, Israel can continue to set an example for other nations in adopting sustainable practices and achieving energy security.

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Machine learning companies in 2020

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Artificial Intelligence Fundamentals : Making Machine Intelligent

Pramod ChandrayanFollow

Founder & CEO Mobibit : A Mobile App & Web Development Company | Startup evangelist | Technology Consultant | Growth-hacker | Mentor | Tech Blogger

Sep 10

Image Source : weblizar.com

Humans And Machines:

We human beings are the most sophisticated living gadget on this mother earth, We are the most powerful intellectual machine which has it’s own intelligence to make decisions, our intellect made sure we ruled over all other living creatures on this planet. We learned to acquire all the skills 

which was necessary for our survival but once our survival process was 

ensured we started to explore more, our infinite intelligence which knows no boundaries wanted more. We started to invent tools which will help us save time for yourself and ensure more safety and security, gradually we ventured to invent machines which can be an extension to our intellectual brain and memorize more information and multitask for us .

The very first computing machine called Turing-Complete was invented by Charles Babbage in 1833, since then computer machine has transformed from Analog era to Digital Era.

AI Origin :

The concept of AI was introduced by John McCarthy together with Marvin Minsky, Allen Newell and Herbert A. Simon. McCarthy coined the term "artificial intelligence" in 1955, and organized the famous Dartmouth Conference in Summer 1956. This conference started AI as a field.

Why Artificial Intelligence :

The very basic questions which needs to be addressed here before we jump on to the definition of Artificial Intelligence is Why Artifical intelligene ? when we human being has got this supremely powerful computing brain. Why the virtual intelligence is required even at first place when we have the intellect of our own. Well it all coin downs to one word “Decision Making”, it was conceptualised because we human being felt that we can’t be present every where and can’t take decisions remotely without being present at that place which became necessary specially after Industrial revolutions and now it has became a necessity after digital revolutions where information are being generated in trillions of bytes.To process this information and to reach to some virtual conclusion we needed that computer machine to think and perform like human brains and so the Artificial intelligence was founded as an academic discipline in 1956, and in the years since has experienced several waves of optimism,followed by disappointment and the loss of funding (known as an “AI winter”), followed by new approaches, success and renewed funding. For most of its history, AI research has been divided into subfields that often fail to communicate with each other.However, in the early 21st century statistical approaches to machine learning became successful enough to eclipse all other tools, approaches, problems and schools of thought.

What is Artificial Intelligence :

AI is a the theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.

We want our machine to think like human, giving them the intelligence of their own by feeding them lots of information simulating the environment similar to our real world, given the fact that they can process huge information in splits of a second. This entire AI computing is to facilitate humans to perform their task with more efficiency where they get an opportunity to become extremely comfortable (for me it’s lazy) and rely on machine to make decisions on their behalf .

AI Application :

AI Research is majorly focussed in developing intelligent programs which can simulate human intelligence in the field of

Reasoning

Learning

Problem Solving

Natural Language Processing(NLP)

Perception Building

Ability to move & manipulate objects

Image Source : cognitivescale.com

Types of Artificial Intelligence :

There are basically 4 types of AI

Weak AI (narrow AI) – non-sentient machine intelligence, typically focused on a narrow task (narrow AI)

Strong AI– (hypothetical) sentient machine (with consciousness and mind).

Artificiall General Intelligence (AGI) – (hypothetical) machine with the ability to apply intelligence to any problem, rather than just one specific problem, typically meaning "at least as smart as a typical human".

Superintelligence – (hypothetical) artificial intelligence far surpassing that of the brightest and most gifted human minds.

Some Of The Popular AI Development Tools Are :

Mostly there are opensource Developer tools which has been built by aspiring opensource community and some of the notable opensource tools being utilised in Artificial Inteligence to make our machine have their own brains are

OpenAir : OpenAIR is a message routing and communication protocol for AI systems that has been gaining in popularity in recent years (2006)

OpenCog : It is a project that aims to build an open source AI framework. OpenCog Prime is an architecture for robot and virtual embodied cognition that defines a set of interacting components designed to give rise to human-equivalent AGI as an emergent phenomenon of the whole system.

OpenIRIS : OpenIRIS is the open source version of IRIS, a semantic that enables users to create a "personal map" across their office-related information objects. The name IRIS is an acronym for "Integrate. Relate. Infer. Share.". It was built as part of CALO project, a very large AI funded by the Defense Advanced Research Projects Agency (DARPA) under its Personalized Assistant that Learns program.

4. RapidMiner : It is a platform of a Data Science awhich came into ezistenve in 2006 and has mainly be used for business and commercial applications as well as for research, education, training, rapid prototyping, and application development and supports all steps of the machine learning process including data preparation, results visualisation, model validation and optimization

Nutshell :

We human being has the greater responsibilities for building an intelligent machines which will not replace us or snatch our jobs but help us become more efficient and Intelligent human being ourselves. We don’t need to fear the machines as they are just a piece of hardware and can’t have the Intelligence until we assist and collaborate with them to work responsibly.

Whats Next : In continuation to this AI series we will deeply cover each tool which are largely being used to help us build this super Intelligent machines and also cover Deep Learning which is the most futuristics and advanced Machine Learnig techique.

To be Continued…..

Thanks for being there with me !

Resource: https://medium.com/towards-data-science/artificial-intelligence-fundamentals-making-machine-intelligent-d3f28f236c7