Solar Energy Management System Forecasting Peak Generation Profiles
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Solution Overview
Problem
Existing energy management systems lack the ability to forecast solar energy peak generation profiles, determine real-time energy consumption patterns, and provide guidance to users on how to align their energy usage with solar energy generation, leading to unnecessary energy cost overruns and increased CO2 emissions.
Innovation Solution
A system and method that forecast solar energy peak generation profiles based on real-time and forecasted weather data, determine user energy consumption patterns, and provide proactive advice to users on modifying their energy usage to match the solar energy generation profile, thereby avoiding energy consumption peaks and offshoots.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If current energy management systems provide only charts and tables of energy consumption performance, then the system complexity remains low, but the energy cost savings and behavioral change guidance are insufficient
Solution Approach 1:
The system implements closed-loop feedback by continuously monitoring energy consumption data, comparing it against forecasted solar generation profiles, and providing real-time alerts and recommendations to users. This feedback mechanism enables users to adjust their energy consumption patterns dynamically, achieving up to 35% cost savings while maintaining system simplicity through automated analysis.
Solution Approach 2:
The system performs preliminary forecasting of solar energy generation peaks and offshoots before they occur, allowing users to proactively adjust their energy consumption patterns in advance. By predicting peak generation times and potential offshoots ahead of time, the system enables preventive optimization rather than reactive adjustments.
2Loss of energy
If the system forecasts solar energy peak generation profiles and provides real-time consumption pattern analysis, then energy cost savings increase significantly, but the device complexity and computational requirements increase
Solution Approach 1:
The system performs self-service by automatically collecting weather data, forecasting solar generation profiles, analyzing consumption patterns, and generating recommendations without requiring complex user configuration or manual intervention. This automation reduces the perceived complexity for users while maintaining sophisticated analytical capabilities in the background.
Solution Approach 2:
The system integrates multiple functions into a single platform: weather data collection, solar generation forecasting, real-time consumption monitoring, pattern analysis, alert generation, and recommendation provision. This multi-functionality consolidates what would otherwise require multiple separate systems into one unified solution.
3Object-generated harmful factors
If the system provides detailed forecasts and real-time monitoring capabilities, then the ability to reduce CO2 emissions improves, but the loss of information processing time and computational resources increases
Solution Approach 1:
The system implements periodic monitoring and forecasting cycles, updating solar generation predictions and consumption pattern analyses at regular intervals rather than continuously. This periodic approach maintains accurate real-time insights while reducing computational overhead and processing time requirements compared to continuous analysis.
4Loss of energy
If the system predicts and provides guidance on avoiding consumption offshoots and peak shaving, then energy optimization improves, but the ease of operation for users decreases due to more complex interactions required
Solution Approach 1:
The system acts as an intermediary by providing automated analysis and recommendations rather than requiring users to directly manage complex energy optimization parameters. The system translates sophisticated forecasting and analysis into simple, actionable insights that users can easily understand and implement without needing to understand the underlying complexity.
Data Source
AI summary
A computer implemented method for managing and optimizing solar energy consumption of a user includes retrieving weather data over a predetermined time period from weather resources connected to the internet. Solar energy generation ratings of a solar energy system of a user are obtained. A solar energy peak generation profile over the predetermined time period is forecast for the solar energy system. Energy consumption ratings of a plurality of devices used by the user are obtained that are powered by the solar energy system. An energy consumption pattern of the user's use of the plurality of devices over the predetermined time period is determined. One or more offshoots of the energy consumption pattern are predicted to occur. The one or more offshoots of the energy consumption pattern are avoided by modifications of the user's use of a set of devices of the plurality of devices during the predetermined time period.