Building Energy Load Forecasting using Deep Neural Networks (1610.09460v1)

Abstract: Ensuring sustainability demands more efficient energy management with minimized energy wastage. Therefore, the power grid of the future should provide an unprecedented level of flexibility in energy management. To that end, intelligent decision making requires accurate predictions of future energy demand/load, both at aggregate and individual site level. Thus, energy load forecasting have received increased attention in the recent past, however has proven to be a difficult problem. This paper presents a novel energy load forecasting methodology based on Deep Neural Networks, specifically Long Short Term Memory (LSTM) algorithms. The presented work investigates two variants of the LSTM: 1) standard LSTM and 2) LSTM-based Sequence to Sequence (S2S) architecture. Both methods were implemented on a benchmark data set of electricity consumption data from one residential customer. Both architectures where trained and tested on one hour and one-minute time-step resolution datasets. Experimental results showed that the standard LSTM failed at one-minute resolution data while performing well in one-hour resolution data. It was shown that S2S architecture performed well on both datasets. Further, it was shown that the presented methods produced comparable results with the other deep learning methods for energy forecasting in literature.

• The paper presents deep neural networks, specifically LSTM and S2S models, to enhance forecasting accuracy of building energy consumption.
• It compares standard LSTM with S2S architecture, highlighting S2S’s superior performance on both one-hour and one-minute data resolutions.
• The findings suggest that advanced deep learning models can significantly improve smart grid energy management and reduce energy wastage.

Building Energy Load Forecasting Using Deep Neural Networks

Building energy consumption is a significant concern for sustainability, representing a substantial portion of global energy usage and wastage. This paper addresses the issue of energy load forecasting in buildings by exploring the application of Deep Neural Networks (DNN), specifically focusing on Long Short-Term Memory (LSTM) algorithms. The research highlights the necessity for precise predictions of future energy demand, which is crucial for efficient energy management within smart grid systems.

The paper investigates two LSTM variants: standard LSTM and LSTM-based Sequence to Sequence (S2S) architecture. These models were applied to a benchmark electricity consumption dataset of one residential customer, analyzed at both one-hour and one-minute time-step resolutions. The primary aim was to evaluate their performance in predicting future building energy loads.

Key Findings

• Standard LSTM Performance:
  • This model demonstrated proficiency in forecasting energy load at a one-hour resolution but encountered difficulties with one-minute resolution data, indicating limitations in handling finer temporal granularity.
• LSTM-based Sequence to Sequence (S2S) Architecture:
  • The S2S approach achieved commendable performance across both one-hour and one-minute data resolutions. This architecture's capability to accept inputs of arbitrary lengths allows for flexibility in forecasting multiple future time steps effectively.
  • The S2S model produced results comparable to prior methodologies like Factored Conditional Restricted Boltzmann Machines (FCRBM), presenting it as a viable alternative in energy forecasting tasks.

Experimental Insights

The experimental results underscore the challenges associated with building-level energy load forecasting, particularly highlighting the inadequacy of standard LSTM models with high-frequency data. The employment of the S2S architecture effectively mitigated these challenges, demonstrating robustness and flexibility in various temporal resolutions. Regularization techniques, such as Dropout, further enhanced model generalizability on testing datasets.

Implications and Future Directions

The findings of this paper have both practical and theoretical implications. Practically, the improved accuracy of short-term load forecasting could lead to better demand response strategies and energy management in smart grids, potentially reducing wastage and improving efficiency. Theoretically, this research provides a foundation for exploring more sophisticated architectures or combining different deep learning methodologies to further enhance predictability.

Future research directions could involve evaluating these models on diverse and real-world datasets to confirm their applicability in various contexts. Additionally, exploring hybrid approaches that integrate other machine learning techniques with deep learning models might yield more accurate and reliable forecasting systems.

In conclusion, this research contributes to the domain of energy consumption forecasting by providing an alternative deep learning-based methodology that addresses some of the inherent challenges of building-level load prediction. Such advancements are crucial for developing smarter, more efficient, and sustainable energy systems.