Abstract
This paper presents a novel deep learning architecture for short-term load forecasting of building energy loads. The architecture is based on a simple base learner and multiple boosting systems that are modelled as a single deep neural network. The architecture transforms the original multivariate time series into multiple cascading univariate time series. Together with sparse interactions, parameter sharing and equivariant representations, this approach makes it possible to combat against overfitting while still achieving good presentation power with a deep network architecture. The architecture is evaluated in several short-term load forecasting tasks with energy data from an office building in Finland. The proposed architecture outperforms state-of-the-art load forecasting model in all the tasks.
Highlights
Due to increasing utilization of variable renewable energy (VAE), demand flexibility is becoming crucial part of the stabilization of smart grids
This paper presented a novel neural network architecture style, called Stacked Booster Network (SBN), for short-term energy load forecasting
This is achieved with following key ideas, which have driven the design of the SBN: (1) sparse interactions, (2) parameter sharing, (3) equivariant representations, and (4) feedback of residuals from past forecasts
Summary
Due to increasing utilization of variable renewable energy (VAE), demand flexibility is becoming crucial part of the stabilization of smart grids. Neural Networks (ANNs) are the most commonly used machine learning model in short-term forecasting of building energy loads. We propose a novel hierarchical neural network architecture for short-term load forecasting that has been designed to address the abovementioned limitations of state-of-the-art load forecasting models. The core idea is to reduce the model parameter space with following principles: (1) sparse interactions, (2) parameter sharing, and (3) equivariant representations Another key idea of the architecture is a novel boosting technique, which makes it possible to transform the original multivariate time series problem into univariate one. The proposed boosting technique enables the model to correct systematic mistakes by utilizing residual information on historical forecasts With these ideas we can build a deep learning framework for short-term load forecasting with following properties:.
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