Open AccessProceedings Article
Statistical Learning versus Deep Learning: Performance Comparison for Building Energy Prediction Methods
Paige A. Mynhoff,Elena Mocanu,Madeleine Gibescu +2 more
- 01 Sep 2018
9
TL;DR: The analysis of the day-ahead and weekahead energy prediction demonstrates that different prediction methods present significantly different levels of accuracy, with the DBN offering the most consistent performance over various lookahead horizons and resolutions.
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Abstract: In this paper, deep learning methods are compared with traditional statistical learning approaches for the purpose of accurately predicting the electrical energy consumption at the building level. Despite the fact that a wide range of machine learning methods have already been applied to energy prediction, deep learning methods certainly represent the state-of-the-art in artificial intelligence, and have been used with remarkable success in a wide range of applications. In particular, the use of Deep Belief Network (DBN), Multi Layer Perceptron and Artificial Neural Network methods are considered in this work. Furthermore, deep learning performance is compared with the most commonly used statistical learning methods, such as Support Vector Machines, Hidden Markov Models and Factored Hidden Markov Models. The analysis of the day-ahead and weekahead energy prediction demonstrates that different prediction methods present significantly different levels of accuracy, with the DBN offering the most consistent performance over various lookahead horizons and resolutions. The methods are validated with the Pecan Street large-scale dataset that comprises an interesting mix of consumer behaviors, electrical vehicles and photovoltaic generation.
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Citations
A Novel CNN-GRU-Based Hybrid Approach for Short-Term Residential Load Forecasting
Muhammad Sajjad,Zulfiqar Ahmad Khan,Amin Ullah,Tanveer Hussain,Waseem Ullah,Mi Young Lee,Sung Wook Baik +6 more
TL;DR: The proposed model is an effective alternative to the previous hybrid models in terms of computational complexity as well prediction accuracy, due to the representative features’ extraction potentials of CNNs and effectual gated structure of multi-layered GRU.
415
An efficient data model for energy prediction using wireless sensors
TL;DR: This paper proposes a system based on Multilayer Perceptron (MLP) to predict energy consumption of a building using collected information from a Wireless Sensor Network (WSN) and achieves state-of-the-art results.
74
Deep learning application in smart cities: recent development, taxonomy, challenges and research prospects
Amina Nuhu Muhammad,Ali M. Aseere,Haruna Chiroma,Habib Shah,Abdulsalam Ya'u Gital,Ibrahim Abaker Targio Hashem +5 more
TL;DR: Recent progress, new taxonomies, challenges and opportunities for future research opportunities on the application of deep learning in smart cities have been unveiled and can provide opportunities for experts in the research community to propose a novel approach for developing the research area.
72
A state-of-the-art review on artificial intelligence for Smart Buildings
Rav Panchalingam,Ka Ching Chan +1 more
TL;DR: The volume of research conducted to-date into AI technologies for use in Smart Buildings is skewed towards some AI topics such as machine learning, neural networks and pattern recognition over other Topics such as deep learning and natural language processing.
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Electricity Consumption Forecasting Using Gated-FCN With Ensemble Strategy
Aqdas Naz,Nadeem Javaid,Muhammad Asif,Muhammad Umar Javed,Abrar Ahmed,Sardar Muhammad Gulfam,Muhammad Shafiq,Jin-Ghoo Choi +7 more
TL;DR: In this paper, an eight-layered Fully Convolutional Network (FCN-8) was used for short-term load forecasting and an Enhanced Bidirectional Gated Recurrent Unit (EBiGRU) for long-term temporal dependencies of the time series.
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