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Build Smart Cities Using AI and ANAI

The unparalleled growth of population the world over in a couple of decades has led to a surge in demand for resources from electricity, petroleum, water, and other natural resources. This unprecedented demand, leading to depletion of resources calls for systematic, optimal, long-term resource planning in place to match and provide future demands. An improvement in planning will result in building newer smart cities which will be more innovative, safer, sustainable, and resilient.

Smart cities strive for sustainability by making the most of limited resources. Providing and maintaining resources that can be allocated to everyone in the city necessitates careful planning and analysis that takes into account a variety of features. To bridge this gap between urban and smart cities, machine learning techniques are being implemented, to optimize sustainability.

The topics and dimensions of sustainability being solved by AI solutions are covered here in this post, as well as how to get closer to the sustainable development goals is discussed. The major goal is to develop a methodology that will enable us to address these issues in the present and the future. Applicability, sustainability scopes and dimensions, tools, data formats, and Machine Learning approaches are all discussed in depth.


With the help of advanced technology such as various sensors and cameras, the collection of data becomes quite easy and efficient. Later, managing that data, data warehousing, analysis, and EDA on the collected data can be used to understand patterns and factors that can be taken into consideration for further decision making and ML training.

Implementing machine learning algorithms opens up a spectrum of horizons, for example, with various predictive and forecasting techniques multiple use cases can be worked on such as predicting the weather, pandemic spreads, and also resources required for a city. Accordingly coming up with strategies to meet demand and reduce wastage of resources, should be one of the primary goals of the smart cities and is also a requirement of current times.

This case study demonstrates how the ANAI platform can perform machine learning on an energy usage data set to show how the platform can be used for managing such complicated data sets with ease and how advanced ML models can be trained on them within a few clicks.

The data selected shows the energy consumption in the City of Chicago for households, businesses, and industries. We selected the feature, ‘ TOTAL KWH (Total KWH from Accounts)’ as the target variable that we want to predict using other independent features available in the data.

The task at hand is a regression problem that will use other features to predict a continuous value i.e. ‘TOTAL KWH’. Hence, we will know the value of energy consumed provided some other inputs to these regression models.

Implementation of Chicago data set via ANAI

ANAI performs a whole range of operations, completely automated which include ML-based data engineering, automated feature transformation and extraction, automatic training of ML models to give the best out of them, with added explanations for the model predictions, and a deployment/monitoring strategy for the ML model. As a result, a bunch of highly accurate ML models is trained and the best one can be selected and then deployed wherever the software will be used.

The above image depicts the output provided by ANAI after running the Chicago data set through the AutoML pipeline. The random forest regressor performed the best in predicting our target variable “TOTAL KWH” and is the recommended model by ANAI.

This model will be now able to predict the total KWH usage given the other parameters as an input to it. The predicted values can then be used to manage the city power resources and grid management to plan ways of using resources optimally and setting up foundations to support smart cities and their efficiency in the future. Similarly, just selecting another target value can create an AI solution for some other use case according to the need of that city.
From, gained predictions some potential changes can be made to reduce energy usage, and wastage and can be used as a path towards sustainability. Such models can help city planners to:
– Set up better infrastructure,
– Energy optimization through advanced forecasting and planning,
– Use slightly less efficient renewable resources with full functionality and dependability.

A look into how the models think:

Below, the chart shows a Permutation feature importance graph that displays the significance of each of the features in making a prediction. At the top, ‘TOTAL THERMS’ is affecting the model the most and the effect decreases as we move down.

Such charts are a part of ANAI’s Explainable AI feature, which tries to give out explanations for a model’s outcome so that we can have a look inside the model’s decision-making process and also so that we can eliminate or rectify models with an over-reliance on the wrong features for a prediction.

Business Impact

A report from PwC UK (How AI can enable a sustainable future) states that greater use of AI could reduce global greenhouse gas emissions by as much as 4% by 2030, equivalent to around 2.4 gigatons of carbon dioxide making our cities more breathable, healthy and productive. Google also claims its DeepMind technology has reduced its data center bill by 40%, something that can be leveraged in smart cities to reduce the city’s power requirements.

AI-powered smart cities will provide companies and individuals with access to a pleasant work environment, which leads to life transformation for a better future. Smart cities are imagined with science and lifestyle at the forefront of their development. This also helps in maintaining the productivity of a city’s economy in various ways.

Over the next two decades, it is anticipated that infrastructure investment for smart cities would total around $40 Trillion. This funding will help convert 40 worldwide cities into the smart cities of the future. Building on this opportunity to make better cities and sustainable development possible, AI can be leveraged to produce models that work with this objective in mind. But to implement AI without the challenges, ANAI comes up with an effective solution which was also demonstrated in the case study above

Some key factors that result in smart cities are:

Increased Sustainability – A city’s current resources have a substantial environmental effect, and a smart city constantly strives to minimize this and can attain zero carbon footprints.

Driving Innovation – We can already see homes and businesses being outfitted with AI / IOT equipment that has made everyone’s lives simpler. This is in response to the growing desire for technological reliance. The scope and expansion of enterprises will undoubtedly expand as technology advances.

Boost the global economy – Businesses will also be able to exploit data acquired by smart city appliances and equipment to better understand their target demographic space and deliver better services.

● Enhanced Governance – Citizens increasingly demand strong, robust, user-friendly digital solutions to support their everyday lives. Collaboration tools, sophisticated cognitive websites, self-service portals, mobile apps, and simple online banking have become the norm in many facets of life, and tech-savvy residents demand no less from their city.

Increase Workforce Engagement – A highly productive staff is critical to the success of any organization. Smart technology deployment helps to reduce the manual burden that annoys every urban professional.


The case study focused on sustainability as a key aspect of smart cities, how having smart cities can ensure optimal and efficient use of natural resources, using the data available as a foundation to plan and design smart cities, addressing any possible future energy variability, disease outbreaks, resource shortage, etc.
The link between smart cities and machine learning was demonstrated here. The case study was designed to optimize sustainability in smart cities. ANAI’s machine learning models and predictions were analyzed and drawn insights to make visible the platform’s capabilities.

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