Heat Pump Power Scheduling for Forecasted Thermal Demand
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Solution Overview
Problem
Current electric heat pump control systems fail to adjust electrical consumption output to match forecasted energy consumption and favorable electricity prices, as they do not account for expected thermal demand, leading to inefficient energy usage and suboptimal adaptation to variable energy tariffs.
Innovation Solution
A data-driven approach using historical data from heat generators, combined with weather forecasts, employs machine learning to predict electrical and thermal energy requirements, allowing for automatic adjustments in load models to optimize energy usage based on specific consumer needs and current operating states, without requiring manual input of building parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by stationary object
If heat pump control is based on current thermal demand specifications, then thermal energy supply is ensured, but electrical consumption cannot be optimized according to forecasted energy prices and renewable availability
Solution Approach 1:
The system performs preliminary forecasting of thermal energy demand using historical data and weather forecasts before actual consumption occurs. This enables advance planning of heat pump operation to align with favorable electricity prices and renewable energy availability, rather than reacting to current demand alone
Solution Approach 2:
The control system dynamically adjusts the electrical output setpoint based on real-time conditions including forecasted thermal demand, current electricity prices, and renewable energy generation. This creates a flexible control approach that adapts to varying conditions rather than following fixed thermal demand specifications
2Productivity
If heat pump operation is increased to meet thermal demand, then thermal energy supply is ensured, but electrical consumption increases without optimization for low price periods
Solution Approach 1:
The system calculates forecasted thermal energy demand in advance and uses this information to plan heat pump operation during periods of low electricity prices or high renewable availability. Thermal storage capacity is utilized to decouple thermal production from immediate consumption, allowing electrical consumption to be shifted to more favorable time periods
Solution Approach 2:
The control system continuously monitors actual thermal demand, compares it with forecasted values, and adjusts the electrical output setpoint accordingly. This feedback mechanism ensures thermal supply requirements are met while optimizing electrical consumption based on actual operating conditions and forecast accuracy
3Reliability
If thermal storage capacity is increased to bridge low solar radiation periods, then heat availability is ensured, but system complexity and storage requirements increase significantly
Solution Approach 1:
The system uses forecasted thermal demand and weather data to determine optimal heat storage requirements in advance, rather than oversizing storage capacity to cover worst-case scenarios. This allows adequate heat availability while minimizing storage system complexity and cost
Solution Approach 2:
The control system dynamically adjusts operational parameters including heat pump output, storage charging/discharging rates, and timing of thermal energy production based on forecasted conditions. This flexible parameter adjustment replaces the need for large, complex storage systems with intelligent control of smaller storage capacity
Data Source
AI summary
A method for controlling an electric heat pump for heating a building using a power schedule, comprising the following steps: • Evaluation of data transmitted by the heat pump and the associated control technology to a data acquisition instance, • Acquisition of historical weather data at the location of the heat pump, • Combination of the collected time series data using data analysis, • Creation of a model of heat generators and consumers, • Adaptation of the load model to specific characteristics of individual consumers, • Checking whether circumstances require a re-evaluation, • Creation of a power schedule from the forecast of the electrical energy demand and control of the heat pump accordingly.