Utility Consumption Pattern Analysis for Peak Load Shifting
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Solution Overview
Problem
The increasing variability in energy demand due to factors like peak usage periods and the integration of electric cars and heat pumps strains local electrical grid transmission, necessitating efficient management of energy consumption and generation to avoid costly infrastructure upgrades.
Innovation Solution
A method analyzing utility consumption data to identify recurring patterns and divergences, allowing for the calculation of diagnostic measures that adjust energy supply and consumption patterns, thereby optimizing grid management and reducing the likelihood of power outages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If infrastructure is updated to increase network capacity, then grid transmission capability is improved, but construction cost and time increase significantly
Solution Approach 1:
The patent changes the operational parameters of the grid by implementing dynamic pricing schemes and demand response programs. By adjusting price signals and consumption patterns rather than physical infrastructure, the system achieves improved transmission capability utilization without costly construction projects.
Solution Approach 2:
The patent replaces mechanical/physical infrastructure expansion with information-based control systems. Smart meters, communication networks, and automated control algorithms substitute for physical grid upgrades, enabling capacity management through data-driven decisions rather than concrete construction.
2Power
If energy supply is increased to meet peak demand, then power availability is improved, but energy waste during off-peak periods increases
Solution Approach 1:
The patent implements periodic pricing structures and demand response cycles that encourage consumers to shift usage patterns. By creating time-varying incentives (peak/off-peak pricing, real-time pricing), the system periodically adjusts consumption behavior to match supply conditions, ensuring power availability during peaks while reducing waste during off-peak periods.
Solution Approach 2:
The patent establishes feedback loops where consumption data from smart meters is continuously monitored and used to adjust pricing signals and control strategies. This real-time feedback enables dynamic optimization of supply-demand matching, preventing energy waste by adjusting supply to actual consumption needs rather than maintaining constant high-capacity supply.
3Measurement precision
If consumption data is monitored with high granularity, then measurement precision is improved, but data processing complexity increases
Solution Approach 1:
The patent segments the data processing task by implementing hierarchical architectures where smart meters perform local data collection and preliminary processing, regional systems aggregate and analyze patterns, and central systems perform high-level optimization. This segmentation reduces the computational burden at any single point while maintaining high measurement precision through distributed processing.
Solution Approach 2:
The patent introduces intermediary systems (data aggregation layers, pattern recognition algorithms, predictive models) that mediate between raw high-granularity consumption data and control decisions. These intermediaries pre-process and summarize detailed data into meaningful patterns and predictions, reducing the complexity of subsequent analysis while preserving measurement precision where needed.
Data Source
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AI summary
A method and apparatus for analysing utility consumption at a utility supply location is described. The method comprises the steps of: receiving utility consumption data corresponding to utility consumption at the utility supply location over a time period to be analysed; generating a recurring consumption model indicative of repeating consumption patterns in the utility consumption data; identifying divergences between the utility consumption data and the recurring consumption model; computing a diagnostic measure indicative of irregular consumption based on the identified divergences; and outputting the diagnostic measure. The diagnostic measure may be used to identify flexibility or irregularities in consumption and/or to control supply of the utility. The utility may be e.g. electricity, gas or water.