Deep Neural Network Forecasting for Building Peak Demand Mitigation
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Solution Overview
Problem
Existing building management systems rely on manual configuration and reaction to peak power demand, leading to inefficiencies and increased energy costs, as they are not compatible with various intelligent devices and lack proactive demand management strategies.
Innovation Solution
The implementation of artificial intelligence and deep neural networks for continuous forecasting and dynamic control of building systems to anticipate and mitigate peak power demand, using circuit-based sensors and cloud analytics to identify and adjust energy usage proactively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If traditional building management systems are used with manual configuration and reaction-based control, then device compatibility is limited and operational simplicity is maintained, but energy cost efficiency deteriorates due to peak-time price hikes and response delays
Solution Approach 1:
The system employs machine learning models that automatically analyze power consumption data, identify peak demand patterns, and generate control strategies without human intervention. The building management system serves itself by autonomously optimizing energy usage, selecting controlled devices, and adjusting settings to reduce peak demand charges while maintaining operational simplicity for users.
Solution Approach 2:
The system continuously monitors real-time power consumption data from multiple devices and uses this feedback to train and refine machine learning models. The feedback loop enables the system to learn from historical data, predict future peak demand events, and dynamically adjust device control strategies to minimize energy costs while adapting to changing building conditions and occupancy patterns.
2Loss of energy
If facilities managers manually monitor and respond to peak power demand alerts, then operational simplicity is maintained, but response time deteriorates leading to higher energy costs
Solution Approach 1:
The machine learning models predict upcoming peak demand events before they occur by analyzing historical power consumption patterns, weather forecasts, and occupancy data. The system proactively implements control strategies in advance of predicted peak periods, pre-adjusting device settings to reduce demand charges before the actual peak event hits, thereby eliminating response delays associated with manual monitoring.
Solution Approach 2:
The system automatically detects peak demand conditions and executes control actions without requiring facilities manager intervention. The autonomous operation eliminates the time lag between manual alert reception and human response, enabling instantaneous automated adjustments to device operations when peak demand conditions are detected or predicted.
3Loss of energy
If building management systems control individual devices to reduce peak demand, then energy cost efficiency is improved, but operational complexity and device compatibility requirements increase
Solution Approach 1:
The system employs a universal communication interface and standardized data protocols that enable compatibility with multiple device types and manufacturers. The machine learning model aggregates power consumption data from diverse devices through a unified interface, allowing the system to control various building devices (HVAC, lighting, appliances) without requiring device-specific integration complexity, thereby maintaining broad adaptability while reducing energy costs.
4Measurement precision
If deep neural networks are deployed for continuous power demand forecasting, then forecasting accuracy is improved, but computational resource requirements and system complexity increase
Solution Approach 1:
The system segments the forecasting task by deploying distributed computing resources across multiple servers or cloud infrastructure rather than concentrating all computational requirements in a single location. The machine learning models are divided into separate training and inference components, with data preprocessing, model training, and prediction generation handled as distinct modular tasks. This segmentation reduces the computational burden on any single system component while maintaining high forecasting accuracy through coordinated distributed processing.
Data Source
AI summary
A method of managing energy by use of processing logic that comprises a load processor as a cloud service is provided. The method includes receiving power load information from a data collection system located at a building and using a cloud analysis layer that employs machine-learning and artificial intelligence for optimization control, analyzing the received power load information to disaggregate load waveform signals and identify device-based power loads by use of a neural network to perform historical device demand and performance analysis to generate device-based demand forecasting, generating demand forecasts for the building to mitigate peak demand based on analysis of a power draw signal and the generated device-based demand forecasting, and determining whether the generated demand forecast for the building is to peak in a near future, based on threshold values of at least one of generated device-based demand forecasting, power price or cost information, and user behavior analysis.