Month: May 2019

Smart cities are no longer a futuristic concept. In cities such as Berlin, they are fully operational today and pushing the bounds of how the IoT links business, public infrastructure and people all together.  Many cities are introducing a wide range of connected smart city applications, including multiple installations of surveillance cameras, connected waste management control, lighting, parking, traffic control, public transport, and pollution and weather monitoring. We’re also seeing innovations like remote patient care from healthcare providers, improvements to production line efficiency from manufacturers, fleet tracking and control from logistics firms: the possibilities of the smart city are many, varied and growing.

Yet, the growth of smart cities will slow if resources are not invested in developing the fundamental backbone of these projects: effective mobile coverage systems. This includes 4G and eventually 5G coverage, as well as low-power wide area network (LPWAN) connectivity which can support hundreds of millions of smart city sensing devices at a low cost.

The connectivity problem
However, where opportunity lies, so does adversity. The smart city connectivity problem is split between three major stakeholders: operators must provide a coverage and capacity solutions to multiple sites across a city within a budget; venue owners/businesses must also provide coverage and capacity to ensure they do not lose out on revenue from customers due to poor mobile connection in their facilities; and local governments need to work with both the operators and venue owners to ensure coverage is supplied across the city to develop sustainable, competitive smart city infrastructure.

The urban environment exacerbates the challenges of deploying coverage. Firstly, building materials used to construct densely-populated smart cities often contain reflective and dense materials that can prohibit all Radio Frequency (RF) energy from propagating within many structures. Green building initiatives also have requirements that impact RF signals. They are often attenuated through increased use of Low-E glass (metals in glass) and reflective (foil backed) insulation.

Basements and parking garages are further examples, as they are usually surrounded by concrete and rebar preventing good RF signals reaching inside. Equally, while a building itself might be constructed with materials that support strong RF signals, buildings within a dense urban area can often be shielded by neighbouring buildings causing poor coverage.

The solution for smart connectivity in cities

An energy efficient, cost-effective and scalable solution which will support smart city models, IoT and M2M applications and mobile users is needed. Furthermore, networks must be able to provide robust public safety communications for emergency services to prevent harm and keep people safe in these new ‘smart’ environments.

Smart cities place incredibly weighty coverage demands on networks. As people and businesses grow increasingly connected to each other and technology through the IoT, service providers need to invest wisely in technology which not only allows for a better experience for all subscribers but also ensures a robust communications network is in place for the emergency services.

Ensuring long-term connectivity for smart cities

Driven by sensors, networks and data-analytics, connected cities are centred on real-time information. To support this, sensors are deployed across a range of environmental conditions – for example in streetlights, smart utility grids, and chemical detection systems which provide vital statistics on how the city is performing as well as areas in which they need to be more efficient. With a physical infrastructure – such as the IoT – this can enable service providers to effectively analyse and make use of the generated data. However, if they are to achieve their full potential, these units will need to be protected from the environmental elements. Whilst these systems and sensors are intelligent, it remains essential to maintain connectivity at all times in order to keep the network on and functioning. Otherwise, these new cities will face device and network failure that could be have significant consequences for its citizens.

The potential applications of machine learning and AI in construction are vast. Requests for information, open issues, and change orders are standard in the industry. Robotics, AI, and the Internet of Things can reduce building costs by up to 20 percent. These robots use cameras to track the work as it progresses. AI is being used to plan the routing of electrical and plumbing systems in modern buildings. Other sectors have used AI and other technologies to transform their productivity performance. Construction, in comparison, has progressed at a glacial pace.

AI and Machine Learning for Smart Construction

The potential applications of machine learning and AI in construction are vast. Requests for information, open issues, and change orders are standard in the industry. Machine learning is like a smart assistant that can scrutinize this mountain of data. It then alerts project managers about the critical things that need their attention. Several applications already use AI in this way. Its benefits range from mundane filtering of spam emails to advanced safety monitoring.

AI in Construction
1. Prevent cost overruns

Most mega projects go over budget despite employing the best project teams. Artificial Neural Networks are used on projects to predict cost overruns based on factors such as project size, contract type and the competence level of project managers. Historical data such as planned start and end dates are used by predictive models to envision realistic timelines for future projects. AI helps staff remotely access real-life training material which helps them enhance their skills and knowledge quickly. This reduces the time taken to onboard new resources onto projects. As a result, project delivery is expedited.

2. AI for Better Design of Buildings Through Generative Design

Building Information Modeling is a 3D model-based process that gives architecture, engineering and construction professionals insights to efficiently plan, design, construct and manage buildings and infrastructure. In order to plan and design the construction of a building, the 3D models need to take into consideration the architecture, engineering, mechanical, electrical, and plumbing (MEP) plans and the sequence of activities of the respective teams. The challenge is to ensure that the different models from the sub-teams do not clash with each other. The industry is trying to use machine learning in the form of generative design to identify and mitigate clashes between the different models generated by the different teams in the planning and design phase to prevent rework. There is software that uses machine learning algorithms to explore all the variations of a solution and generates design alternatives. It leverages machine learning to specifically create 3D models of mechanical, electrical, and plumbing systems while simultaneously making sure that the entire routes for MEP systems do not clash with the building architecture while it learns from each iteration to come up with an optimal solution.

3. Risk Mitigation

Every construction project has some risk that comes in many forms such as Quality, Safety, Time, and Cost Risk. The larger the project, the more risk, as there are multiple sub-contractors working on different trades in parallel on job sites. There are AI and machine learning solutions today that general contractors use to monitor and prioritize risk on the job site, so the project team can focus their limited time and resources on the biggest risk factors. AI is used to automatically assign priority to issues. Subcontractors are rated based on a risk score so construction managers can work closely with high-risk teams to mitigate risk.

4. Project Planning

An AI Startup launched in 2018 with the promise that its robots and artificial intelligence hold the key to solving late and over budget construction projects. The company uses robots to autonomously capture 3D scans of construction sites and then feeds that data into a deep neural network that classifies how far along different sub-projects are. If things seem off track, the management team can step in to deal with small problems before they become major issues. Algorithms of the future will use an AI technique known as “reinforcement learning.” This technique allows algorithms to learn based on trial and error. It can assess endless combinations and alternatives based on similar projects. It aids in project planning since it optimizes the best path and corrects itself over time.

5. AI Will Make Job sites More Productive

There are companies that are starting to offer self-driving construction machinery to perform repetitive tasks more efficiently than their human counterparts, such as pouring concrete, bricklaying, welding, and demolition. Excavation and prep work is being performed by autonomous or semi-autonomous bulldozers, which can prepare a job site with the help of a human programmer to exact specifications. This frees up human workers for the construction work itself and reduces the overall time required to complete the project. Project managers can also track job site work in real time. They use facial recognition, onsite cameras, and similar technologies to assess worker productivity and conformance to procedures.

6. AI for Construction Safety

Construction workers are killed on the job five times more often than other laborers. According to OSHA, the leading causes of private sector deaths (excluding highway collisions) in the construction industry were falls, followed by struck by an object, electrocution, and caught-in/between. A Boston-based General Contractor with annual sales of $3 Billion is developing an algorithm that analyzes photos from its job sites, scans them for safety hazards such as workers not wearing protective equipment and correlates the images with its accident records. The company says it can potentially compute risk ratings for projects so safety briefings can be held when an elevated threat is detected.

7. AI Will Address Labor Shortages

Labor shortage and a desire to boost the industry’s low productivity are compelling construction firms to invest in AI and data science. A 2017 McKinsey report says that construction firms could boost productivity by as much as 50 percent through real-time analysis of data. Construction companies are starting to use AI and machine learning to better plan for distribution of labor and machinery across jobs. A robot constantly evaluating job progress and the location of workers and equipment enables project managers to tell instantly which job sites have enough workers and equipment to complete the project on schedule, and which might be falling behind where additional labor could be deployed. Experts expect construction robots to become more intelligent and autonomous with AI techniques.

8. Off-site Construction

Construction companies are increasingly relying on off-site factories staffed by autonomous robots that piece together components of a building, which are then pieced together by human workers on-site. Structures like walls can be completed assembly-line style by autonomous machinery more efficiently than their human counterparts, leaving human workers to finish the detail work like Plumbing, HVAC and Electrical systems when the structure is fitted together.

9. AI and Big Data in Construction

At a time when a massive amount of data is being created every day, AI Systems are exposed to an endless amount of data to learn from and improve every day. Every job site becomes a potential data source for AI. Data generated from images captured from mobile devices, drone videos, security sensors, building information modeling (BIM), and others have become a pool of information. This presents an opportunity for construction industry professionals and customers to analyze and benefit from the insights generated from the data with the help of AI and machine learning systems.

10. AI for Post-Construction

Building managers can use AI long after the construction of a building is complete. Building information modelling, or BIM, stores information about the structure of the building. AI can be used to monitor developing problems and even offers solutions to prevent problems.

The Future of AI in Construction
Robotics, AI, and the Internet of Things can reduce building costs by up to 20 percent. Engineers can don virtual reality goggles and send mini-robots into buildings under construction. These robots use cameras to track the work as it progresses. AI is being used to plan the routing of electrical and plumbing systems in modern buildings. Companies are using AI to develop safety systems for worksites. AI is being used to track the real-time interactions of workers, machinery, and objects on the site and alert supervisors of potential safety issues, construction errors, and productivity issues.

• Intelligent and Efficient Smart grid is capable of sensing system overloads and rerouting power to prevent or minimize a potential outage. It is efficient and potentially able to meet increasing consumer demand without adding any infrastructure.
•  Accommodating Due to its robustness, a smart grid can accommodate energy from fuel sources as well as RE sources and adopt any new technologies for a climate-friendly society.
• Reduce Global Warming Possible to integrate large-scale RE into the grid that reduces global warming as well as GHG emission.
• Repairing and Maintenance Automatic maintenance and operation increase the efficiency of the power network. Moreover, predictive maintenance and self-healing reduce system disturbances.
• Reliability Improves power quality and reliability as well as enhances the capacity of the existing network.
• Distributed Generation Accommodates distributed power sources efficiently which reduces energy costs, GHG emissions and energy crisis issues worldwide.
• Consumer Focus Consumers can customize their energy uses based on individual needs, electricity prices and environmental concerns.
• Security With the adoption of security features in the smart grid, the network is safer from cyber-attack and any unwanted tampering and natural disaster. 1.9. Quality-Focused Ensures power quality of the network by reducing voltage fluctuation (sag, swell and spikes) and harmonic effects in the network.
• Technology New concepts and technologies will be developed that enhance power system infrastructure and accommodate new opportunities in innovation.
• Socio-Economic Development This new technology will open new doors in the power sector and communication arena. It will play an active role in socio-economic development as well as create job opportunities.

Smart grid promotes energy saving

•  Improve the utilization efficiency of power generation resources by optimizing scheduling
  Optimization scheduling technologies have been already extensively applied in renewable energy
  generation such as wind, solar, PV systems and distributed generation.  proposes a novel solution for
  generation scheduling problem in power systems with a large capacity of grid-connected wind energy.
•  Reduce line loss by optimizing reactive power compensation With the development of high voltage and long-distance transmission systems, reactive power in power lines is increasing rapidly, causing problems such as voltage loss and deterioration of power quality. It is significant to utilize reactive compensation to regulate voltage and improve the power quality of the power system. In a smart grid, main technologies for reactive power compensation include capacitor control and FACTS. Intelligent reactive power compensation technologies by capacitor control include network communication technology, zero cutting technology, etc.
•  Improve the utilization efficiency of electrical equipment by demand response (DR) DR is a market behaviour which makes a response to market price or incentive mechanism to change ordinary electric consumption. Advanced information, control and communication technologies are integrated with the smart grid, which provides strong technical support to DR projects. Key techniques of DR
  are as follows:

1.  Smart meters, record the real-time data on the electricity use and provide data support for the DR.
2.  Two-way communication technology can complete real-time and high-speed information interaction.
3. Home domain network, enhance the user participation in DR project.
4.  User measurement data management, improve the accuracy of load forecasting and help to formulate
   reasonable DR project. Improve the power consuming efficiency

• Smart grid improves the power consumption efficiency of power users . The smart grid provides all
  kinds of the necessary information for electric power users, such as current and historical electricity
  consumption, carbon dioxide emissions from the consumption of electrical energy, instant demand,
  environment temperature, humidity, and illuminance, etc. The electricity information is fed back to the
  users, which helps the users adjust the power consumption mode, change the concept of power
  consumption to improve the efficiency, and promotes the access of distributed power to the grid as well,
  so as to achieve smart interaction and green energy saving.
• Energy sustainability contributes to climate and environment sustainability
  Climate warming influences human living conditions and natural environment, which is the basis for
  social and economic development, at the same time, the rapid development of the society aggravates the environmental pollution, and also GHG emissions that result in climate warming. Climate change has
  negative impacts on ecosystem services, human health and many other areas. Climate and environment
  sustainability have strong coupling with energy sustainability. The sustainable climate and environment
  are helpful for realizing the efficient utilization of energy supply and security, energy sustainability has a
  closed-loop positive feedback effect on the climate and environmental sustainability.

Building technology is the largest consumer of energy after transport and power generation. Heating, cooling and lighting in residential and office buildings make up about 40 percent of the energy consumed in a building. Smart buildings are key in a world where ongoing urbanization will force building owners to strive for more efficiency and sustainability. Artificial Intelligence (AI) has the potential to enhance commercial building automation by sensing and analyzing information about where and how people use the space, while maintaining highest standards for privacy and data security. Intelligent Building Control systems to residential and non-residential buildings, can create energy savings of up to 50 percent.

Artificial Intelligence puts the ‘smart’ in smart buildings

Artificial intelligence (AI) is poised to fundamentally change the way we use technology to solve challenging problems. From the potential of self-driving cars to virtual assistants like Apple’s Siri or Amazon’s Alexa, we are already starting to see a glimpse of what the future holds. While it is still early on for many consumer AI applications, AI is being deployed for a range of business applications that have the potential to be big revenue generators and money savers.

IoT by increasing the intelligence of sensor solutions used in:

• Heating, ventilation and air conditioning (HVAC)
• Gas and water supply systems
• Temperature sensors
• Humidity sensors
• Air quality sensors
• Vibration sensors
• Early problem detection
• Variable air volume systems

AI continues to infiltrate the market, below are ways in which it can be used to make buildings smarter.

1. Predictive Energy Optimization
When it comes to reducing energy consumption, buildings are reliant on after-the-fact reporting, essentially analyzing what energy was used and then implementing a change in the hope that less energy will be used next time. AI and predictive analytics are disrupting this in favor of a moe proactive approach.

Controlling room temperature within a building is like controlling speed when riding a bicycle. Many forces change the speed of a bicycle when it is in motion.

In the case of a heating and cooling (HVAC) system, there are numerous thermal loads that influence the temperature of a space. To cool a room, the system blows cold air into the space to decrease the temperature.

However, other thermal loads such as human activity, solar radiation, and heat from electronics increase room temperature. When these loads add up to zero, the room temperature is fixed.

AI-based energy management platforms can identify the “uphills” and “downhills” for building operations by applying AI in the form of machine learning to advanced models of a building’s thermal characteristics.

It will identify when it makes sense to precool the building to avoid energy use during hours when energy is at the highest price (the uphill), or when to decrease cooling due to periods of inactivity within a building based on historical usage patterns (the downhill).

2. Preventative Maintenance and Fault Detection
In addition to optimizing day-to-day operations, AI and machine learning can be relied upon for fault detection. AI techniques are well-suited in learning the relationship between input and output variables using only data, without mathematical models. This technology can excel at analyzing data from various systems and IoT devices within a building to identify anomalies and inconsistencies. After identifying these symptoms, AI can be used to target a diagnosis.

In an ideal world, data anomalies would be automatically detected by AI-algorithms, and then immediately triaged and to identify the root cause. However, within a building there is a deeper issue of resource constraint. There are often a lot more subtle and qualitative aspects to detection issues that require a person to filter.

3. Improving Tenant Comfort
Using AI to optimize building operations and prevent faults will inherently create a more comfortable environment for tenants. Exploring the relationship between comfort, direct tenant feedback, and AI is perhaps one of the more recent developments in smart buildings.  Companies are actively racing to find the best ways to personalize comfort for individuals within a shared workplace. While there is no clear-cut path to how this will develop in the future, it is certain that humans act as the ultimate sensor within a building.

Thus, integration of mobile apps – and perhaps wearables – will likely have a large role in the way tenants interact with buildings.

The future of AI in buildings is bright but human expertise will always be needed to properly utilize and direct the technology.

The building space has been traditionally slow to adopt new technologies but embracing AI-based solutions is inevitable as it capitalizes on the boom in the adoption of IoT-driven devices within facilities.

Smart cities, make you think of an ecosystem that is completely interconnected, seamlessly. Cities of the future will be truly smart when they are built on a unified platform – that can support smart lighting and every other device. It is what will make the city energy-efficient too. Cities need a smart sensor platform network. It is what provides the connection from a sensor or field device to a centralized IoT or data collection platform. Through it, the city’s infrastructure collects data and uses it for decision-making purposes.

Smart Sensors for cities:

The network and platform established for smart street light control, for example, will not only serve the primary application, but also other sensors and applications, forming a “smart sensor platform network” for other systems across domains such as environment, traffic, public safety, metering, and waste,

Having different applications connected to a shared smart sensor platform network provides the opportunity for data collection and analytics across different domains.

This also provides new insights that a single domain vertical cannot offer. For example, it helps gauge how rain can affect traffic flow and street light control.

Urban Sensing

Using CCTV cameras and video analytics to anonomously detect people (so the system knows a person walked down a street but doesn’t know who that person is) is one example of smart city sensors in action. The people, bicycle and vehicle counts are uploaded over the internet to “Brokers”. The Brokers process the data and makes it available to all end users – comprising an Internet of Things. Such a system is supplied by our sister company – Retail Sensing.

Sensing Pedestrians

In the high streets and city centres, VT Smart Counters are counting people. They provides both real-time and historical data for “Big Data” analytics.

They can also be used within individual retail units to discover vital analytics like sales conversion rate, average queuing time and the most popular area of a store. This demonstrates in a practical way the city’s commitment to retail on the high street.

The Smart Counters are attached to lampposts around the city.

Sensing Bicycles

The Smart Counter can also be used to count bicycles. This helps monitor and support the promotion of healthy travelling and gives a measure of how green or pollution-free areas are within a city centre.

Sensing Vehicles

Smart Counts accurately count cars down roads and at junctions. Councils can effectively manage the flow of traffic along the busiest routes across the city and monitor the days and times of the heaviest flow.

Combining the Counts and Scoring Effectiveness of Smart City Projects

The Internet-of-Things data provided helps quantify the use of footpaths and cycle ways. It shows the use of roads, including commuter routes around schools and major routes through the city centre. This can help score the effectiveness of Smart City projects , which aims to build and deliver a smarter, more connected Manchester. Creating a city that uses technology to meet the complex needs of its people.

From a health perspective Smart City data to monitor the effectiveness of sports activity, events and jogging routes within parks.

Counting on Public Transport

The Smart Counters are not just being used around the city streets. Buses, trains and trams can also benefit.

Sensing Passengers on Buses

Transport authorities can know the numbers of people arriving by bus at various points in the city, by time of day. The data helps revenue protection – reconciling tickets bought with passenger numbers. It also enables effective fleet bus management with services around the city centre. With real-time GPS location of buses, it gives a clear picture of what is going on.