The Role of AI and ML in Solar Energy

The Role of AI and ML in Solar Energy !!

Climate change is one of the biggest threats that humanity is currently facing. With these challenges which lie before the government and various energy solution providers, it has become imperative for them to provide a sustainable mode of renewable energy. this has also made renewable energy to be an alternative source of energy compared to fossil fuels. It is much safer and cleaner than conventional sources. 

Artificial intelligence (AI) and machine learning (ML) have become important technology solutions as the industry is constantly looking for ways to cater to the rapidly increasing demand for clean, cheap, and reliable energy. These advanced technologies have the potential to analyze the past, optimize the present, and predict the future. This means that AI and ML have the potential to solve most of the challenges that currently prevail. 

How Can AI Technology Help?

The electric grid is one of the complex machines when it comes to renewable energy. Grids that are currently available face many challenges in accommodating the diversity of renewable energy because of the integral variability of wind and solar. This makes it necessary to have smart systems that can expand the integration of renewable's into the existing grid and make renewable energy an equal player in the energy supply. Here’s how AI technology improves the reliability of renewable energy and modernizes the overall grid.

What does the future hold?

According to a recent paper published by DNV GL, AI will increasingly automate operations in the coming years in the solar and wind industries and boost efficiencies across the renewable energy sector.

An increasing number of sensors are expected to be installed along with the increase in easier-to-use ML based tools, and the continuous expansion of data monitoring, processing and analytics capabilities to create new operating efficiencies.

AI in treating cancer

AI in treating cancer !!

One of many relatable AI applications for social good pertains to the healthcare sector, as it may personally affect us all. Cancer causes 1 in 6 deaths globally, an estimated 9.6 million people died in 2018, and over 300.000 cases are diagnosed each year. As the disease keeps spreading, AI approaches for healthcare are quickly establishing revolutionary solutions in this space.

Predictive analytics techniques are being applied to healthcare scenarios, in order to evaluate clinical data and anticipate future trends. One of the main advantages of predictive analytics in this field is in improving the accuracy of diagnosis and treatment.

PathAI, a technology provider for pathology laboratories, found a strong correlation between AI-powered and manual qualification of one particular protein across various tumor cells. The PD-L1 protein keeps immune cells from attacking non-harmful cells in the body, and since some cancer cells contain high amounts of this protein, they can deceive the immune system and avoid detection as harmful agents. Thus, they are not attacked. By precisely identifying that protein, health professionals have a greater understanding, which in turn gives them the ability to determine if a patient is more likely to develop cancer or not.

An AI model prediction was evaluated and compared with manual assessments by a network of pathologists to discover whether the platform performed consistently. Researchers trained a model with more than 250,000 pathologist-provided annotations, which eventually performed successfully at the level of a certified pathologist, as stated in the press release. This finding may save vital time and resources by forecasting the probability that cancer cells will spread in a patient.

AI/ML applied to improve sanitary conditions

AI/ML applied to improve sanitary conditions

Also related to livelihood and health conditions, over a billion people in the world are living in urban areas lacking basic sanitation services, water, and/or electricity. It is expected that as the global population grows by the millions, 1 in 4 people on the planet will live in an urban settlement by 2030, without access to essential services.

Bangalore is one of the most crowded cities in India. Home to more than 8 million people, around 8% of the city’s population lives in slums. This reality inspired deep learning research that strives to segment and detect those geographical movements.

In this study, researchers found that the first step in rehabilitating crowded areas is by mapping and monitoring field dynamics. Previously, those tasks were carried out manually by human annotators and consumed a vast amount of time and effort. The focus of this application was to automate an inefficient process used to identify changes in satellite images. Doing so will make it easier to monitor how those geographical areas evolve. The study explored the potential of fully convolutional networks (FCNs) to analyze the temporal dynamics of small clusters of temporary slums using very high resolution (VHR) imagery.

AI techniques in wildlife conservation

AI techniques in wildlife conservation

It is expected that 38% of the species as we know of will be extinct by 2100. In fact, just a few days ago, the first animal extinction of the new calendar year was confirmed: the Chinese paddle fish. This threat applies to Africa too, as Panthera leo has lost over 40% of their natural terrain over the last 20 years. The loss of land has forced animals to roam more extensively across a very fragmented landscape, which difficult's the tracking efforts.

Several AI applications for wildlife conservation include computer vision solutions as their primary thrust. In particular, image recognition and classification processes have had significant positive animal preservation results. The approach was along the same lines.

Historically conservationists identify lions by manually analyzing precise whisker patterns in each animal. Facial whiskers do not change frequently throughout an animal’s life, and this makes it possible to recognize each individual animal over time, by observing their whisker patterns alone. This thorough examination was performed by conservationists, who had to compare whisker photographs with a predefined grid and isolate the unique design that matched the specific animal being analyzed from a database of over 400 lions.

The tool chains consist of computer vision and pattern recognition algorithms that can automatically perform the different methods for lion identification: face and whisker id. This reduces time spent and the number of human resources required allowing conservationists to viably use large data sets in their work.

Introduction

       

Introduction To AI / ML !!

What is Artificial Intelligence (AI)

AI deals with the area of developing computing systems which are capable of performing tasks that humans are very good at, for example recognizing objects, recognizing and making sense of speech, and decision making in a constrained environment.

Narrow AI:

                  The field of AI where the machine is designed to perform a single task and the machine gets very good at performing that particular task. However, once the machine is trained, it does not generalize to unseen domains. This is the form of AI that we have today, for example Google Translate.

Artificial General Intelligence (AGI):

                                                             A form of AI that can accomplish any intellectual task that a human being can do. It is more conscious and makes decisions similar to the way humans take decisions. AGI remains an aspiration at this moment in time with various forecasts ranging from 2029 to 2049 or even never in terms of its arrival. It may arrive within the next 20 or so years but it has challenges relating to hardware, energy consumption required in today’s powerful machines, and the need to solve for catastrophic memory loss that affects even the most advanced deep learning algorithms of today.

Super Intelligence: 

                                Is a form of intelligence that exceeds the performance of humans in all domains. This refers to aspects like general wisdom, problem solving and creativity.

Classical Artificial Intelligence: 

                                                     Are algorithms and approaches including rules-based systems, search algorithms that entailed uninformed search (breadth first, depth first, universal cost search), and informed search such as A and A* algorithms. These laid a strong foundation for more advanced approaches today that are better suited to large search spaces and big data sets. It also entailed approaches from logic, involving propositional and predicate calculus. Whilst such approaches are suitable for deterministic scenarios, the problems encountered in the real world are often better suited to probabilistic approaches.

The field has been making major impact in recent times across various sectors including Health Care, Financial Services, Retail, Marketing, Transport, Security, Manufacturing and Travel sectors.

The advent of Big Data, driven by the arrival of the internet, smart mobile and social media has enabled AI algorithms, in particular from Machine Learning and Deep Learning, to leverage Big Data and perform their tasks more optimally. This combined with cheaper and more powerful hardware such as Graphical Processing Units (GPUs) has enabled AI to evolve into more complex architectures.

Machine Learning 

Machine Learning is defined as the field of AI that applies statistical methods to enable computer systems to learn from the data towards an end goal. The term was introduced by Arthur Samuel in 1959.