From the combination of both technologies with the smart home devices of the Spanish consumer electronics brand comes this groundbreaking and innovative development that enables the leap from a connected home to a truly intelligent and autonomous home: self-learning, self-regulating, and self-governing.
This new ecosystem of SPC smart devices is powered by the most innovative AI, Reinforcement Learning (RL), and Big Data to offer greater energy efficiency for our home and more savings on electricity or gas bills.
Big Data collects information from weather forecasts and historical home measurement data to understand how we can achieve greater energy efficiency. Meanwhile, RL applied to SPC’s smart home devices enables them to learn from their own experience and improve their behavior to reach the target temperature at the lowest possible cost.
SPC, the Spanish technology company specializing in smart and consumer electronics devices, presented at Mobile World Congress 2023 (MWC) an ecosystem of IoT devices powered by a groundbreaking and innovative technology that combines future AI, Reinforcement Learning (RL), with Big Data. These two technologies, together with SPC’s smart home devices, represent the definitive step from a connected home to a truly intelligent home, a fully autonomous home capable of making decisions that provide the user with better energy efficiency and greater savings on electricity and gas bills.
To achieve this advancement in our homes, the company collaborates with BigDa Solutions, the data-driven startup recognized for offering intelligent energy management solutions to large industrial plants. In this partnership, the BigDa Solutions team contributes its expertise gained over more than 7 years of experience in the industrial sector for developing predictive models and energy forecasts aimed at optimizing energy consumption. Now, thanks to this collaboration, SPC makes all the knowledge acquired by its partner and the technologies previously used in large industry available to the end consumer, making them accessible to everyone.
On its part, SPC contributes not only its complete ecosystem of smart climate control devices for our homes (thermostats, smart valves, infrared device controllers, among others) but also deep knowledge of the technologies needed to create smart home products, acquired over more than five years of developing these types of devices.
Big Data collects and manages weather insights and historical home measurement data
The first vertex of this triangle is Big Data, the technology that collects and manages information about weather forecasts, as an external factor affecting the environment where the SPC device is located, as well as all the historical home measurements collected by it.
To establish these weather forecasts, a global 3D mesh system is used that generates the forecast and defines conditions of temperature, humidity, pressure, wind… whose evolution over time can be predicted through atmospheric dynamics using numerical weather prediction models (NWP). These NWPs allow for "zooming in" (a downscaling technique) to determine what happens at a specific geographic point, such as in our city.
Thus, this innovative project can “anticipate” the weather conditions that will occur at a very precise geographic point, while also having past information on how the device acted under similar conditions.
What does future AI bring to SPC’s smart devices?
Once all that massive, private, and advanced information is stored in Big Data, the second vertex of this development comes into play: Reinforcement Learning, or future AI. The new SPC devices collect home data which, together with weather forecast data, create AI predictive models that use Reinforcement Learning (RL). This branch of artificial intelligence enables the company’s smart home products to have a capacity for individualized learning in their environment (home), which is also their training center where they evolve and improve daily to achieve excellence. And they do this by making decisions on their own based on the home’s habits regarding the systems they control: raising or lowering the temperature of a room, turning heating on/off…
In this way, through continuous training, the devices seek to optimize the achievement of the assigned goal (also known as reward) based on certain criteria. In this case, the reward is savings on electricity or gas bills, and the policy criteria are to maintain the temperature within defined setpoint values for a given period.
Big Data and RL combine with smart home devices to create an autonomous home
SPC uses the combination of large-scale data collection technology and algorithms and techniques to imitate, and even surpass, human intelligence to make the leap from a connected home to a truly intelligent home.
By cross-referencing all the information from Big Data and RL and combining it with SPC’s smart home devices, this intelligent development is born, aiming for the greatest energy savings in an automated and autonomous way.
The only user intervention will be to enter into the device management app the information about the contracted electricity and gas rates and the target temperature they want to reach at a set time so that the ecosystem works mainly during the cheapest time slots.
“The project we have developed will fully automate home energy management regarding climate control, whether for gas or electric heating systems or air conditioning units,” emphasizes Teresa Acha-Orbea, general manager of SPC. The company’s smart devices based on this cutting-edge proprietary technology are completely autonomous: they will be able to self-govern and self-regulate based on their own experience and also thanks to the weather forecasts they use as input. “In this way, they will make our lives easier because we will no longer have to worry about turning them on or off; they will do it themselves, always prioritizing savings and energy efficiency,” adds the SPC executive.
In fact, if weather forecasts predict cold or heat waves, the system will act preventively accordingly, automatically adjusting the home temperature and optimizing energy use to maintain efficient consumption that does not negatively impact the bill, always considering the contracted rates and the times when energy consumption is cheaper. Since the system is self-learning, it has the capacity to learn from its own experience and will become increasingly efficient over time.
This pioneering technological development is another step in SPC’s strategy to be at the forefront of the smart home in Spain, as it was when it became the first Spanish company to develop an IoT product ecosystem for the home under a single standard. Moreover, it represents the definitive step to bring technologies previously reserved only for industrial environments to mass consumption, marking a major advance in energy efficiency management in homes.
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