Energy Management System with Automated Load Forecasting
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Solution Overview
Problem
Current energy management solutions fail to provide a comprehensive view of energy usage and actionable recommendations for businesses and power providers, lacking a technology-agnostic approach to exploit available energy-related data effectively, and are unable to adjust to changing energy conditions.
Innovation Solution
A system utilizing an automated load forecasting engine that collects and analyzes various data types from multiple sources, including geospatial databases, to provide customized energy product offerings and optimize energy resource management, addressing peak usage, cost minimization, and energy conservation objectives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If current energy management solutions are used, then some energy data can be collected, but they fail to provide a comprehensive view of energy usage and actionable recommendations
Solution Approach 1:
The system segments energy management into multiple specialized modules: data collection module, load forecasting module, objective function optimization module, and recommendation generation module. Each module handles specific aspects of energy management, allowing comprehensive analysis without overwhelming system complexity.
Solution Approach 2:
The energy management system is designed as a universal platform that can handle multiple types of energy data sources, various objective functions (cost minimization, peak demand reduction, renewable integration), and different recommendation types, providing comprehensive energy usage insights through a single integrated system.
2Loss of information
If energy management systems are made more comprehensive, then better energy insights are provided, but the systems become less adaptable to changing energy conditions
Solution Approach 1:
The system employs dynamic objective functions that can be adjusted in real-time based on changing energy conditions, market rates, and operational priorities. The load forecasting models are continuously updated with new data, and recommendations are regenerated dynamically as conditions change, maintaining both comprehensiveness and adaptability.
Solution Approach 2:
The system implements continuous feedback loops where actual energy consumption data is compared against forecasted values, and the discrepancies are used to refine future predictions and adjust recommendations. This feedback mechanism allows the comprehensive system to adapt to changing conditions automatically.
3Measurement precision
If automated load forecasting is implemented, then energy usage can be predicted, but implementation costs increase
Solution Approach 1:
The system implements load forecasting at multiple levels of detail: basic forecasting for all customers and advanced forecasting only for high-value or complex cases. This partial application of sophisticated forecasting capabilities reduces overall implementation costs while maintaining necessary accuracy for critical applications.
Solution Approach 2:
The forecasting system allows dynamic adjustment of model complexity parameters based on customer needs and budget constraints. Users can select different levels of forecasting accuracy and corresponding computational resources, enabling cost-effective implementation scaled to specific requirements.
4Adaptability or versatility
If customized energy product offerings are provided, then customer energy needs are better met, but the system becomes more complex
Solution Approach 1:
The system pre-generates a library of standardized energy product offerings and configurations based on common customer profiles and requirements. When a customer's energy needs are analyzed, the system matches them against this pre-prepared library, providing customized recommendations without requiring complex real-time generation of every possible solution configuration.
Data Source
AI summary
In one aspect, a Customer Acquisition method and system are provided to identify target customers and market sizing based on forecasting a customer's energy usage and determining a customized set of energy product offerings to satisfy a customer's energy needs. In another aspect, an Energy Resource Management method and system are provided to select and place energy products to meet the requirements of a particular objective function for a utility, power provider and/or customer. Objective functions can comprise of peak usage reduction, bill or cost minimization, deferred utility upgrades, emissions reduction, efficiency increases, and energy conservation. In still another aspect, an Energy Resource Management method and system are provided for anticipating and estimating implementation costs, optimizing installation strategy and product placement for the purposes of customer cost savings and budgeting.