The Role of Machine Learning for HVAC System Optimization
Commercial HVAC systems tend to be less energy-efficient, noisy, and unpredictable in real-world environments. Advanced technologies like Artificial Intelligence (AI) and Machine Learning (ML) can change the way we think and interact with them. Considering this context, the aim of this article is to discuss the use of Machine Learning for HVAC systems.
Artificial Intelligence and Machine Learning approaches are gaining a stronghold in recent times. However, it’s not so long (around 2005) when the breakthroughs started appearing in AI.
Currently, AI products and services, ー smartphones, automatic vehicles, facial-recognition software, building controls, etc. ー are becoming increasingly prevalent and powerful every day. AI-infused algorithms and functions are also being standardized by time.
However, the transformation is and has not been as simple as it seems. Artificial Intelligence is a diverse domain, consisting of a host of algorithms that imitate the human brain to process complex calculations.
For instance, as is generally the case, commercial buildings experience high energy consumption; this impacts negatively on our economy and the environment. So the building automation industry is presently ahead of the center with acquiring new technologies and capabilities. And, as a matter of fact, improving the living experience in large buildings is coming up with an increased level of complexity.
In these circumstances, Machine Learning, a part of the AI ecosystem, is a highly adaptive solution. Dating back to 2015, when ML was first introduced for emerging technologies since then, it has promising use in Building Management Systems (BMS).
Let’s talk about conventional commercial HVAC controls that are one of the major BMS subsystems. Generally, there are building operators who manage HVAC operations. They analyze the system and employ “rule-based techniques” to “modify” its settings. On the flip side, smart HVAC controls feature “Machine Learning” to “optimize” building temperature automatically, without human intervention.
The Difference Between Machine Learning and Rule-based Systems
Like ML, rule-based systems also belong to Artificial Intelligence, but besides the fact that they have plenty of drawbacks, these systems are still widely used throughout the United States.
Certainly, choosing between the two can be more of a challenge. To help you decide, we’ve prepared a comparison chart below. It highlights the key differences between the two solutions.
|Machine Learning System||Rule-based System|
|Designed to learn and improve over time||Designed and improved by humans|
|Machines can access millions of data points from a variety of sources and react preventively to mitigate risk before it grows up||As human’s ability is limited towards data gathering and processing, rule-based systems operate on a few predefined set of logics only|
|Probabilistic in nature ー ML-based algorithms allow machines to configure building settings based on the current environment. This enables BMSs to respond to different conditions dynamically||Deterministic in nature ー Rule-based systems possess only one configuration (set of already decided parameters) that doesn’t change later. Hence, there’s very little flexibility with environmental control|
|Sustainable design; they are efficient in the long run||Prone to faults; there’s a gradual loss of efficiency|
|Expensive initially||Expensive eventually|
Applications of Machine Learning
According to a latest report by the US Energy Information Administration (EIA), commercial buildings account for 18% of the country’s entire energy loss incurred during electricity generation, transmission, and distribution.
In this context, Machine Learning is such a great tool that has transformed how owners perceive BMS systems. For instance, ML-powered HVAC units leverage an online automated monitor that evaluates the internal/external conditions of a building, its control actions, and the results of those actions 24/7 to ensure maximum reliability.
Here’s a list of characteristics you can look out for when using a Machine Learning-based HVAC system:
- Data Visualization and Clustering
- Demand Response
- Demand Side Management
- Energy Forecasting
- Peak Shaving
- Outage Protection
- Anomaly Detection
- Predictive Control
- Fault Localization and Optimization
- Process Control and Optimization
- Renewable Energy Integration
Why Smart Building Automation is Essential
Smart BMS systems allow building owners to adjust the temperature of the entire building based on the unique preferences of each occupant. This reduces significant amounts of energy and maintenance costs annually. However, the case is only true as long as the system doesn’t start to age (which is a bitter truth).
In real-world circumstances, it is very much essential for operators to keep the BMS from degradation. Although difficult, they must know the key to optimize it in real-time.
Smart building automation refers to the control of building systems, encompassing various components, including not only the HVAC, but lighting, access control, security, fire safety, and a lot more as well. Building Automation Systems (BAS) are used for this purpose. They analyze millions of billions of data points from throughout the building, gather them, and store them into a single large model for better environmental management and optimization.
Some of the data points BASs consider for environment control include:
- Air Flow
- Atmospheric Pressure
The Role of Machine Learning in Building Automation
Large buildings are vulnerable to the risks of failure and breakdown. Machine Learning is a highly-effective technology that can be embedded directly into the BMS for immediate system control. ML algorithms flow throughout the BMS to realize when occupants arrive in different parts of the building and put different strains on its systems.
The obtained data points enable the operators to analyze each equipment from one centralized place and decide how it can be kept optimized. Moreover, if the occupant behavior changes, the algorithm learns and adapts the change and notifies the operator so as to maximize the system performance under all conditions.
How Machine Learning can be Instrumental in HVAC Systems
When it comes to commercial HVAC systems, you can call ’em the biggest culprits of energy consumption, accounting for 40 percent of a building’s total energy consumption. Machine Learning empowers the operators to see the HVAC data of the entire building as a single interrelated unit. Thousands of data points are generated every second, and various patterns are formed to predict and accordingly automate the system behavior.
The benefits of this automation are limitless, to name a few, energy-efficiency, carbon footprint reduction, demand management, and improved Indoor Air Quality (IAQ).
At ActiveBAS, we harness the power of Artificial Intelligence to enhance building control. Our SaaS solution adapts a predictive building energy model that optimizes building controls for energy efficiency and comfort, and prevents system failures and downtimes. Using smart energy saving methods our clients enjoy upto 40% in energy bills savings.
If you’re looking to create a smarter building today, contact ActiveBAS to learn how we can help you smarten up your building at no-upfront costs.
- United Nations Trust Fund for Human Security. (No Date). Human Security: Building Resilience to Climate Threats. Retrieved December 01, 2020, from: https://www.un.org/humansecurity/wp-content/uploads/2017/10/Human-Security-and-Climate-Change-Policy-Brief-1.pdf
- HVAC HESS. (2013, September). HVAC Energy Breakdown. Retrieved December 01, 2020, from: http://www.environment.gov.au/system/files/energy/files/hvac-factsheet-energy-breakdown.pdf