Probabilistic Building Energy Forecasting via LSTM Sequence-to-Sequence Model
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Solution Overview
Problem
Building managers face challenges in accurately predicting energy consumption deviations, which can impact their ability to plan and reduce carbon footprints and electricity costs due to the stochastic nature of time-series forecasts.
Innovation Solution
A building system that uses a probabilistic data point forecasting method, employing a recurrent neural network (RNN) sequence-to-sequence (S2S) model, specifically a long-short term memory (LSTM) S2S neural network, to generate probability distributions for energy consumption data points, allowing for confident predictions of future energy usage ranges.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional time-series forecasting methods are used for energy consumption prediction, then the forecasting process is simple, but the forecast deviations are large and unpredictable due to the stochastic nature of energy consumption
Solution Approach 1:
The patent transforms the forecasting approach by changing the output parameter from a single deterministic value to a full probability distribution. This allows the model to capture the stochastic nature of energy consumption while providing quantifiable uncertainty measures, directly addressing the forecast deviation problem without excessive complexity increase
Solution Approach 2:
The patent replaces traditional mechanical/statistical forecasting methods with a deep learning-based recurrent neural network. This substitution enables the system to learn complex temporal patterns and stochastic behaviors from historical data, improving forecast accuracy while the modular architecture keeps complexity manageable
2Reliability
If probabilistic forecasting with full probability distributions is implemented, then building managers can understand confidence intervals and plan better, but the computational complexity and data processing requirements increase significantly
Solution Approach 1:
The patent segments the probability distribution output into discrete bins or histograms, transforming a continuous complex distribution into manageable discrete categories. This segmentation makes the probabilistic forecasts easier to process and interpret while maintaining the reliability benefits of capturing uncertainty
Solution Approach 2:
The patent implements feedback mechanisms where the probabilistic forecasts are used to guide building operations, and actual outcomes feed back into the model for continuous improvement. This creates a closed-loop system that enhances reliability over time while the feedback structure helps manage complexity through iterative optimization
Data Source
AI summary
A building system for building data point prediction, the building system comprising one or more memory devices configured to store instructions, that, when executed by one or more processors, cause the one or more processors to receive first building data for a building data point of a building and generate training data, the training data comprising a probability distribution sequence comprising a first probability distribution for the building data point. The instructions cause the one or more processors to train a prediction model based on the training data, receive second building data for the building data point, and predict, for one or more time-steps into the future, one or more second probability distributions with the second building data based on the prediction model, each of the one or more second probability distributions being a probability distribution for the building data point at one of the one or more time-steps.