Artificial Intelligence technology|Is solar energy a good option?

With the growing environmental concerns about climate change and the need for decarbonization, many private sector organizations, governments, and civil society have committed to a 100% renewable energy future.

As of late 2016, more than 300 cities, municipalities, and regions including Frankfurt, Vancouver, Sydney, San Francisco, Copenhagen, Oslo, Scotland, Kasese in Uganda, Indonesia’s Sumba island and the Spanish Island of El Hierro have demonstrated that transitioning to 100% RE is a viable political decision.

It is no doubt such ambitious targets to transition to 100% renewables will require new tools, concepts, and technologies to cope with the increased penetration of intermittent renewable energy into the grid. The good news is that technological developments, in the artificial intelligence and analytics space have already created tools and solutions needed to enable the decarbonization of the economy according to the International Renewable Energy Agency (IRENA).

As such, the International Renewable Energy Agency (IRENA) has developed solutions in its recent report on the “Innovation Landscape for a Renewable Powered Future” which provides a toolbox of solutions for policymakers and guidance on how to apply them system-wide in a coherent and mutually-reinforcing way.

In particular, these solutions center around the application of digital technologies such as Artificial Intelligence (AI), big data and analytics in increasing flexibility in the system for larger integration of renewable energy.

According to IRENA, Artificial Intelligence (AI) and big data, the Internet of Things and batteries are innovative solutions that will enable massive solar and wind use and amplify the transformation of the power sector based on renewables.

Why AI, Big-Data, and Analytics?

The increasing electrical loads such as electric cars, energy storage (batteries or pumped hydro) as well as decentralized renewable energy power such as rooftop solar PV systems, commercial solar, and wind power systems will need a more stable grid or a smart grid.

A smart grid is able to learn and adapt based on the load and amount of variable renewable energy put into the grid as a result of having lots of rooftops solar PV, other extra loads to the grid such as electric cars, energy storage (batteries and pumped hydro) and increasing decentralized intermittent renewable energy.

Without a smart system using artificial intelligence (AI), big data and analytics, grid operators will definitely not cope with the changing electrical loads and the increasing penetration of renewable energy into the grid.

Also, at its core, AI is a series of systems that act intelligently, using complex algorithms to recognize patterns, draw inferences and support decision-making processes through their own cognitive judgment, the way people do.

How can AI support the large integration of renewable energy?

Since renewable energy is very intermittent in nature as we would expect because there is no constant wind or solar generation due to weather changes, renewables such as solar and wind can be unreliable and many utility companies utilize energy storage (batteries or pumped hydro) to deal with this issue.

Excess solar or wind power is stored during low demand times and used when energy demand goes high. As a result, AI can improve the reliability of solar and wind power by analyzing enormous amounts of meteorological data and using this information to make predictions and knowing when to gather, store and distribute wind or solar power.

On the other hand, AI used in smart grids can be used to balance the grid especially when rooftop solar and other decentralized renewable energy are involved and put into the grid. AI systems utilizing neural networks or complex algorithms to recognize patterns associated with various loads (electric vehicles or energy storage) and increased rooftop solar or other forms of distributed energy (wind or solar) which can make the system to be unstable. The most efficient way to balance this variability in the system is through AI in analyzing grids before and after they absorb smaller units, and in working to reduce congestion.

The IRENA’s report Innovation Landscape for a Renewable Powered Future explains these new AI tools and digital technologies that will support the deployment of renewables as the power sector complexity continues to increase.

According to IRENA, most of the advances currently supported by AI have been in advanced weather and renewable power generation forecasting and in predictive maintenance. However, in the future, AI and big data will further enhance decision-making, planning and supply chain optimization while increasing the overall energy efficiency of the energy systems.

For the renewable energy sector, AI and analytics can support it in several ways such as better monitoring, operation, and maintenance of renewable energy.

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.