AI|Going solar options

Because of the volatility of global oil prices, the cost of energy will continue to increase proportionately and especially when our energy demand continues to depend on finite fossil fuels. Similarly, the cost of energy for an average building in the USA or globally will continue to increase proportionately when the main source is from fossil fuels because the price for energy continues to increase due to volatility of oil prices. Solar PV and increased connectivity is an option that seems very promising and could help to reduce or mitigate the issue of climate change and increasing energy prices.

The advent of AI in energy management

The advent of new technologies such as big data analytics, machine learning and Artificial Intelligence (AI), robotics and blockchain allows for smart building energy management systems that can provide monitoring made possible through the Internet of Things (IoT), advanced data analytics and via wireless connections.

Looking in the future, solar is likely to be sold as a core part of the smart building concept that includes a building energy management system, energy storage, Electric Vehicle (EV) charging and smart appliances. This makes more sense because sourcing all the energy from solar will help to save more money and help to achieve sustainability. Also, EV and smart appliances can help to balance the grid for instance, electric vehicles can be used as temporary storage to connected appliances to reduce power usage when needed.

Also, in the energy management space, lighting and HVAC integration are the two most common systems integrated into the smart building strategy to reduce the energy footprint, but the IoT industry has opened the door to more sensors and hence increased intelligence through data collection. Some of the most common IoT sensors have applications for smart metering, occupancy sensors, water detection, humidity sensors, contact sensors, and carbon monoxide detection among many others.

The whole idea of making your building smart is to allow you to make more informed decisions about the building based on the data it provides. Data is aggregated via IoT (Internet of Things) controls and sensors in a web-based platform that can be monitored, controlled and acted upon in real-time or perhaps using your cellphone. The main advantage of having a smart building is to help facility and property managers gain insight into the detailed workings of their locations and gather useful data to improve building performance and efficiency.

Advantages of integrating solar in a smart building:

Smart buildings utilize machine learning algorithms and can be able to forecast your energy consumption and through demand response mechanisms solar consumption by the building can be increased in times of high solar generation and vice-versa. Through IoT smart appliances can be remotely controlled digitally to adopt on-site demand. For instance, heat pumps, heat storage batteries and air conditioning units can be optimized with solar generation and be a way of using excess solar electricity as heat.
Battery storage and smart electric vehicle charging when integrated with solar PV could significantly increase solar consumption for some households and businesses and especially when solar PV is combined with battery storage.
Deep machine learning and artificial intelligence when integrated with your smart appliances and solar can help to forecast and manage generation and consumption as well as voice activation technology to make systems more user-friendly.
Generally, smart buildings through optimization increase energy efficiency, comfort and safety and with solar PV, more energy is saved reducing your energy footprint.

This article explained how the smart building concept can help to reduce energy consumption and allow for the integration of solar PV, EV charging and IoT helping you reduce your energy footprint to achieve sustainability. However, a key question is whether these smart building technologies can currently pay for themselves? Do they currently increase or decrease the return on investment on installation when combined with solar? EnergySage is a great starting point to help you figure out your energy savings when it comes to going solar.

By 2020, the Fourth Industrial Revolution will be characterized by many technological developments, including but not limited to advanced robotics, autonomous transport, AI, and Machine Learning, advanced materials, biotechnology, and genomics. On the other hand, despite these technological advancements, the world is also experiencing some of the most challenging sustainability issues ever experienced by planet earth such as climate change.

Smart technology combined with AI can help individuals and businesses manage their environmental impact. AI and smart technology can help to collect, analyze data using machine learning to help monitor and improve energy consumption, water consumption while increasing operational efficiency because of lowered of reducing costs and material waste. As such, it is worthwhile to learn how big data and AI can be applied in the industrial, energy, agricultural and water sectors to help monitor and reduce material, water, and energy wastage while increasing operational efficiency.

Below we capture some interesting case studies for how some start-up companies (C3, Falkonry, FogHorn, Sight Machine, SparkCognition, Uptake, Zymergen, Foris.io™, etc) in the industrial sector, as well as well-established companies such as ABB, GE, Siemens, IBM, Honeywell, Hewlett Packard Enterprises, are implementing interesting AI concepts in their respective sectors to help achieve sustainability. National Renewable Energy Laboratory (NREL) is a government research body that is also helping to shape AI and its implementation in the energy sector.

Application of AI in the Industrial Sector.

A combination of software and hardware technology with the use of AI and machine learning is beginning to enter the market and some businesses have adopted such technology to help them manage and improve their environmental performance while saving money.

For instance, in the industrial sector in the US has experienced a high growth of the use of AI-enabled devices to help improve operational efficiency, reduce materials waste, predict interruptions, take advantage of predictive maintenance and optimize resource consumption. The use of AI technology in the industrial sector grows every year at a rate of nearly 65% through 2024.

In the energy sector, the National Renewable Energy Laboratory (NREL) in collaboration with Hewlett Packard Enterprises have developed AI and machine learning technologies to automate and improve operational efficiency, including resiliency and energy usage for data centers. This concept helps to reduce energy consumption and lower operating costs through monitoring and predictive analytics in power and cooling systems for HP data centers.

Application of AI in the Energy Sector.

As such, historical data (about 16 terabytes of data) collected from sensors in NREL”s supercomputers are used to train models for anomaly detection to predict and prevent issues before they occur.

This collaboration will help to address future water and energy consumption as early results based on the models trained with historical data have successfully predicted or identified events that previously occurred and thus can be replicated in other data centers.

Also, AI coupled with smart sensors or devices can help to reduce building energy consumption by up to 30% using accurate sensing and predictive analytics according to Department of Energy (DOE) studies.

Furthermore, ABB has developed machine learning algorithms to predict unplanned peaks in power consumption and identify strategies to prevent them. According to ABB, its Energy Forecasting AI uses neural network methods to identify and learn patterns in a circuit or a building’s energy consumption, while also factoring weather data.

As such, using weather forecasts and historical data, ABB’s Energy forecasting is then able to predict power consumption(kW) for the next 24 hours, updating its forecast every 15 minutes with best-in-class accuracy. This ABB system through accurate power consumption prediction will enable facility managers to take full advantage of Time of Use (TOU) tariffs and to take timely action to reduce unplanned consumption.

With regard to renewable energy, the application of AI and machine learning can help to solve the intermittent nature of wind and solar power that may bring about grid stability issues. Currently, pumped hydro or batteries (energy storage) is employed to solve this intermittent nature of renewable energy.

However, the grid will be more stable when AI and machine learning algorithms are employed with renewable energy to predict or forecast when the amount of solar or wind power goes down so that the power stored can kick in. This can help to avoid expensive or use of fossil fuel-powered standby generators.

Application of AI in the Agricultural Sector.

Similarly, smart sensors and AI can be applied by farmers that have connectivity to save water, energy, fertilizer and pesticide usage. One great example of a company that is implementing the Internet of Things (IoT) to bring improvement in energy and sustainability is Foris.io powered with IBM Watson.

Working with IBM Watson™ and IBMCloud™, foris.io™ uses data from precision agriculture tools and grower records, cognitive computing, and analytics to enable holistic field management.

Using probes installed on the soil, smart devices are capable of measuring and transmitting data on moisture, PH level, salinity, temperature and other factors that are fed into IBM’s data storage, processing, and analytics cloud for analysis.

Using AI and machine learning, the data gained from the soil sensors are combined with a variety of other environmental factors, such as weather, geographic location, crop yield statistics, and other data to provide farmers with real-time feedback to inform about how much to water and fertilize according to Foris.io’s motto, “just enough • just in time”.

Application of AI in the Water sector

According to UN-Water, water scarcity already affects every continent and water use has been growing globally at more than twice the rate of population increase in the last century, and an increasing number of regions are reaching the limit at which water services can be sustainably delivered, especially in arid regions.

In the US, nearly two trillion gallons of water are lost in the country before it even reaches an end-use due to leaks and pipeline faults according to ABB. To address this, ABB amongst many other companies are implementing flow and sound sensors into water infrastructure to pinpoint leaks and predict and proactively maintain the water supply using AI and machine learning.