Kapacity.io Cloud’s controls operate through two key functions: predictive control and load shifting. Let's delve into each aspect, starting with predictive control.
Predictive Control For Heat Pumps
Kapacity.io's predictive control method optimizes energy consumption by leveraging near-term weather forecasts to prevent building overheating. Through the utilization of weather forecasts and machine learning, the system predicts each building's thermal capacity and flexibility. The primary goal is to minimize overheating without causing noticeable discomfort to occupants.
The graph illustrates the alternation of the control (orange) from the baseline control (black dashed), showcasing the system's ability to predict and adjust setpoints. Note that the graph depicts setpoint alternations rather than specific temperature values. The algorithm anticipates reduced heating needs compared to the setpoint.
In the figure, outdoor temperatures increase and thus it is predicted that the building can be heated partly with solar radiation.
This predictive control operates on an individual basis for each building. In the example below, two buildings with identical heat pumps in Helsinki show varying setpoint controls during a typical October day.
The differences in setpoint controls demonstrate the system's adaptability to diverse building setups. Factors such as heating setpoint, occupancy levels, and insulation vary between buildings, and Kapacity.io controls adjust accordingly, aided by machine learning.
Load Shifting To Avoid Peak Prices
Kapacity.io Load Shifting optimizes heating based on electricity prices, aiming for the lowest cost. Up to 30% of households in the Nordics have dynamic electricity prices which means that energy prices change hour-to-hour. These households benefit significantly on smart and predictive control. Consider the example below from a day in October when wholesale electricity prices in Finland showed significant variability, ranging from approximately 0.3 c/kWh during nighttime to as high as 32c/kWh during the day—a hundredfold increase.
The Kapacity.io control (orange) adjusts above the baseline during nighttime and decreases during periods of high prices, ensuring continuous heating without complete shutdown. Notably, hot domestic water consumption remains unaffected, preserving occupants' heating comfort. The energy consumption for this specific day is depicted below.
Results in Energy and Cost Savings
On average, Kapacity.io controls can achieve a 5-15% reduction in energy consumption. When coupled with dynamic electricity price-based control (spot control), users may realize up to a 25% reduction in electricity costs.
Predictive Control: Single-family home customers, on average, have experienced an 8.5% reduction in energy consumption over the last month (ranging from 6% to 11%).
Predictive Control + Load Shifting: With both functionalities, customers have achieved an average cost savings of 13.5% over the last month (ranging from 12% to 17%).
Insights into the Wholesale Electricity Market
Over the past month, wholesale spot prices averaged 32 EUR/MWh, with daily energy prices fluctuating between -1.22 EUR/MWh and 101.39 EUR/MWh in October. Hourly prices ranged from -4.31 EUR/MWh to 221.7 EUR/MWh, making a shift from relatively high prices a sensible choice. Recent numbers can be found here: https://transparency.entsoe.eu/content/static_content/Static%20content/web%20api/Guide.html
Energy Consumption Trends
As outdoor temperatures decrease, energy consumption tends to rise. Precise euro-based savings calculations during periods of low heat pump electricity consumption (below 5kWh daily) may be premature. We will keep you informed and share additional insights on savings in the coming months.
What's in it for me?
If you're interested in trying the Kapacity.io controls out yourself, just sign up for a 30 day free trial here: https://cloud.kapacity.io/