ML Energy Demand Forecasting for Renewable Facility Planning
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Solution Overview
Problem
Organizations face challenges in accurately forecasting energy demand due to fluctuations caused by changes in occupancy, time, season, and operations, leading to inefficiencies and increased costs in purchasing energy resources, often resulting in excessive non-renewable energy usage to prevent outages.
Innovation Solution
A system leveraging artificial intelligence and machine learning to forecast energy demand by utilizing sensor data and historical information, selecting appropriate energy resources, including renewable options, to create a cost-effective and sustainable energy plan.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If organizations purchase excess energy resources to prevent outages due to fluctuating demand, then reliability of energy supply is improved, but energy cost increases
Solution Approach 1:
The system performs preliminary action by forecasting energy demand in advance using machine learning models that analyze historical data, occupancy patterns, weather conditions, and operational schedules. This allows organizations to plan energy purchases ahead of time rather than purchasing excess energy as a precaution, thereby maintaining supply reliability while reducing wasteful spending on unused energy resources.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring actual energy consumption against forecasted demand, analyzing discrepancies, and using this information to improve future forecasts. This closed-loop approach enables the system to learn from past performance and progressively enhance prediction accuracy, allowing for more precise energy purchasing decisions that balance reliability with cost efficiency.
2Reliability
If organizations purchase larger proportion of back-up energy (diesel or non-renewable) to ensure no outages occur, then reliability is improved, but environmental impact worsens
Solution Approach 1:
By forecasting energy demand in advance using multiple input factors including historical consumption data, occupancy patterns, weather forecasts, and operational schedules, the system enables organizations to prepare appropriate energy mixes beforehand. This reduces the need for last-minute purchases of non-renewable backup energy, thereby maintaining supply reliability while minimizing environmental harm.
Solution Approach 2:
The system dynamically adjusts the energy resource mix by changing parameters such as the proportion of renewable versus non-renewable energy based on forecasted demand, available renewable energy generation, and operational requirements. This flexibility allows organizations to maximize renewable energy usage while maintaining sufficient non-renewable backup only when necessary for reliability, thus reducing environmental impact.
3Device complexity
If traditional energy forecasting based only on historical energy use is used, then system complexity is reduced, but forecasting accuracy deteriorates
Solution Approach 1:
The system merges multiple data sources including historical energy consumption data, occupancy sensor data, weather forecasts, operational schedules, and equipment status information into a unified forecasting model. This integration of diverse data streams significantly improves forecast accuracy compared to using historical energy use alone, while the modular architecture manages complexity through organized data processing pipelines.
Solution Approach 2:
The system replaces traditional mechanical or rule-based forecasting methods with machine learning algorithms that automatically learn patterns from multi-source data. This substitution enables the system to capture complex, non-linear relationships between various factors and energy demand, achieving superior forecasting accuracy without proportionally increasing operational complexity.
4Device complexity
If organizations do not accurately forecast energy demand, then device complexity is reduced, but energy resource efficiency deteriorates
Solution Approach 1:
The system performs preliminary energy demand forecasting using machine learning models that analyze multiple factors including historical consumption, occupancy patterns, weather conditions, and operational schedules. This advance planning enables organizations to optimize energy resource allocation, purchase energy when prices are favorable, and minimize waste from both excess purchasing and potential outages, thereby improving overall energy resource efficiency.
Data Source
AI summary
Aspects of the present disclosure provide systems, methods, and computer-readable storage media that leverage artificial intelligence and machine learning (ML) to forecast energy demand and to generate an energy plan for one or more facilities of an organization. For example, a system may forecast an occupancy of the facilities for use with historical demand data in forecasting the energy demand. The forecasting may be performed by one or more trained ML models. Additional ML models may be trained to select energy resources that satisfy the forecasted energy demand and that prioritize constraint(s). The system may generate an energy plan that indicates information related to the selected energy resources, such as cost, energy type, environmental impact, etc., for use in increasing an amount of renewable energy resources used at the facilities. In some implementations, the system may recommend actions to reduce a negative environmental impact associated with the selected energy resources.