We use AI to predict air conditioning demand and optimise air conditioning operation and control.
We aim to achieve significant cost reductions.
THE AI CONTROLLER WILL BE RETROFITTED TO THE CONTROL EQUIPMENT WHILE MAKING USE OF THE EXISTING AIR CONDITIONING SYSTEM. BY USING ARAYA'S AUTONOMOUS AI TECHNOLOGY TO PREDICT THE REQUIRED AIR CONDITIONING CAPACITY (DEMAND) AND MATCH HEAT PRODUCTION TO DEMAND AND ELECTRICITY TARIFFS, WE AIM TO ACHIEVE SIGNIFICANT COST SAVINGS (AROUND 20%).
Solution for District Heating and Cooling (DHC) and Large Heat Source System
WE OFFER THE FOLLOWING SERVICES TO DISTRICT HEATING AND COOLING (DHC) AND LARGE HEAT SOURCE OPERATORS, INDIVIDUALLY FOR EACH BUILDING. (WE CAN ALSO PROVIDE CENTRAL AIR CONDITIONING FOR SOME BUILDINGS.
AUTONOMOUS AI FOR OPTIMAL CONTROL
AUTONOMOUS AI IS A METHOD IN WHICH THE AI ITSELF LEARNS BY TRIAL AND ERROR WHAT ACTION (AIR CONDITIONING CONTROL) IS BEST (COST SAVING).
When the AI takes action (e.g. turning on/off the number of heat production machines in operation), the state of the air conditioning environment (e.g. changes in heat storage and cold supply) changes, and the AI is rewarded if a good result is achieved (e.g. lower heat production costs).
▶︎The AI learns by trial and error to get more rewards.
TWO ADVANTAGES OF USING AI FOR AIR CONDITIONING CONTROL
Advantage 1: Use of a wide variety of input data
Weather forecast data and data obtained from IoT devices and surveillance cameras can be used as control inputs for more detailed control.
Advantage 2: Data-driven parameterisation
Multiple parameters such as set temperature and operation time can be predicted based on past and present data instead of empirical rules, and detailed optimum settings can be made.
Experiment 1: Reduction of annual heat production costs by approx. 20 % for large heat sources
Facilities where the demonstration tests were carried out
Demonstrations were carried out with the following large heat sources
Heat production machines: around 5
Heat storage tank: Yes
Customer: Commercial facility (approx. 60,000 m2)
Cooling water demand (daily average): approx. 200 GJ in summer, approx. 30 GJ in winter
Discounts are available for reduced usage during peak hours.
・Annual heat production cost: 20-40 million yen
Scope of the demonstration
Simulation of heat production only (demand forecasting is done using rule-based algorithms in the existing system)
Verification of heat production cost savings in the simulation
Results of the demonstration
The annual heat production costs could be reduced by about 20% (on simulation).
The cost savings are particularly high in summer, when heat production is avoided at more expensive times of the year.
| Season | Percentage reduction |
|---|---|
| Reduced electricity costs (winter) | 5 to 10 per cent |
| Reduction of electricity costs (summer) | 15 to 22.5 |
DEMONSTRATION EXPERIMENT 2: HIGHLY ACCURATE DEMAND FORECASTING IN DISTRICT HEATING AND COOLING (DHC)
Facilities where the demonstration tests were carried out
Demonstrations were carried out in the following district heating and cooling (DHC)
Heat production machines: around 5
Heat storage tank: Yes
Customers: Commercial facilities and office buildings (total: approx. 70,000 m2)
Cooling water demand (daily average): approx. 300 GJ in summer, approx. 30 GJ in winter
Annual heat production cost: 50-100 million yen
Scope of the demonstration
Forecasting of demand only (the forecast is displayed on the screen and instructions for heat production are given by the operator on a rule-based (manual) basis)
Forecasting with an error within the capacity of one heat production machine (approx. 1000MJ/30min)
Results of the demonstration
It was possible to predict demand with a very high accuracy at a practical level (on simulation).
This allows the operators on site to give instructions for heat production without delay.
| Item | Prediction accuracy after 30 minutes | Prediction accuracy after 1 hour | Prediction accuracy after 24 hours |
|---|---|---|---|
| Chilled water system load heat value | 99.7% | 99.8% | 91.6% |