Building Management System Energy Procurement Automation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Manual energy procurement in building management systems is prone to human errors and is time-consuming, leading to increased operational costs due to the volatility of the energy market and the inability to dynamically switch between energy suppliers based on real-time pricing and energy needs.
Innovation Solution
A building management system connected to an energy blockchain network that monitors energy pricing, calculates energy demand forecasts, generates smart contracts for energy supply agreements, and automatically switches between energy suppliers using an energy load balancer to optimize energy procurement in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual energy procurement is used, then human control and decision-making are maintained, but human errors increase and procurement time extends to 15-20 days or more
Solution Approach 1:
The system enables autonomous energy procurement where the building management system automatically monitors energy pricing, calculates demand forecasts, generates smart contracts, and switches between suppliers without human intervention. This self-service mechanism eliminates human errors and reduces procurement time from 15-20 days to real-time operations.
Solution Approach 2:
The patent replaces manual mechanical procurement processes with automated digital systems including blockchain-based smart contracts, algorithmic demand forecasting, and electronic supplier switching. This substitution of mechanical human operations with automated computational systems resolves the contradiction between reliability and time loss.
2Adaptability or versatility
If manual energy procurement is used, then simple procurement processes are maintained, but dynamic switching between suppliers based on real-time pricing is prevented
Solution Approach 1:
The building management system performs multiple functions including energy monitoring, demand forecasting, smart contract generation, and automated supplier switching within a single integrated platform. This multi-functionality enables dynamic adaptability to pricing changes while managing system complexity through consolidation of procurement operations.
Solution Approach 2:
The system implements dynamic supplier switching based on real-time energy pricing and calculated demand forecasts. The automated procurement process continuously adapts to market conditions by generating new smart contracts and switching suppliers, transforming static manual procurement into a dynamic responsive system.
3Loss of energy
If manual energy procurement is used, then operational control is maintained, but energy costs increase due to inability to respond to market volatility
Solution Approach 1:
The system continuously monitors energy pricing data from multiple suppliers, compares it with calculated demand forecasts, and automatically adjusts procurement decisions by switching suppliers based on real-time market conditions. This feedback loop enables cost optimization through automated response to price volatility, reducing energy costs while implementing procurement automation.
Solution Approach 2:
The system calculates energy demand forecasts in advance and proactively generates smart contracts before procurement is needed. This preliminary action allows the system to secure favorable pricing and switch suppliers ahead of time, optimizing energy costs while managing the automation process efficiently.
Data Source
AI summary
A building management system is communicably connected to an energy blockchain network to reduce energy costs of a building. The system includes an energy load balancer to distribute energy from a plurality of energy suppliers to power a load in the building, a processor, and memory storing instructions that causes the processor to: monitor energy pricing data from each of the energy suppliers; calculate a balanced load payload corresponding to an energy demand forecast for the building according to the energy pricing data from the energy suppliers; generate a smart contract corresponding to the balanced load payload in blocks of the energy blockchain network to procure energy from corresponding ones of the energy suppliers; and generate load balancing instructions for the energy load balancer based on the smart contract. The energy load balancer switches between the corresponding ones of the energy suppliers according to the load balancing instructions.