Related papers: Data-driven building energy efficiency prediction using physics-informed neural networks

Data-driven building energy efficiency prediction using physics-informed neural networks

URL: http://arxiv.org/abs/2311.08035v2
Date: Thu, 25 Apr 2024 15:26:46 GMT
Title: Data-driven building energy efficiency prediction using physics-informed neural networks
Authors: Vasilis Michalakopoulos, Sotiris Pelekis, Giorgos Kormpakis, Vagelis Karakolis, Spiros Mouzakitis, Dimitris Askounis,
Abstract summary: We introduce a physics-informed neural network model for predicting energy performance of residential buildings. A function, based on physics equations, calculates the energy consumption of the building based on heat losses and enhances the loss function of the deep learning model. This methodology is tested on a real case study for 256 buildings located in Riga, Latvia.
Score: 2.572906392867547
License: http://creativecommons.org/licenses/by/4.0/
Abstract: The analytical prediction of building energy performance in residential buildings based on the heat losses of its individual envelope components is a challenging task. It is worth noting that this field is still in its infancy, with relatively limited research conducted in this specific area to date, especially when it comes for data-driven approaches. In this paper we introduce a novel physics-informed neural network model for addressing this problem. Through the employment of unexposed datasets that encompass general building information, audited characteristics, and heating energy consumption, we feed the deep learning model with general building information, while the model's output consists of the structural components and several thermal properties that are in fact the basic elements of an energy performance certificate (EPC). On top of this neural network, a function, based on physics equations, calculates the energy consumption of the building based on heat losses and enhances the loss function of the deep learning model. This methodology is tested on a real case study for 256 buildings located in Riga, Latvia. Our investigation comes up with promising results in terms of prediction accuracy, paving the way for automated, and data-driven energy efficiency performance prediction based on basic properties of the building, contrary to exhaustive energy efficiency audits led by humans, which are the current status quo.

Related papers

The Forecastability of Underlying Building Electricity Demand from Time Series Data [1.3757257689932039]
Forecasting building energy consumption has become a promising solution in Building Energy Management Systems. Different data-driven approaches to forecast the future energy demand of buildings can be found in the scientific literature. The identification of the most accurate forecaster model which can be utilized to predict the energy demand of such a building is still challenging.
arXiv Detail & Related papers (2023-11-29T20:47:47Z)
Application of Zone Method based Physics-Informed Neural Networks in Reheating Furnaces [25.031487600209346]
Foundation Industries (FIs) constitute glass, metals, cement, ceramics, bulk chemicals, paper, steel, etc. Reheating furnaces within the manufacturing chain of FIs are energy-intensive. Accurate and real-time prediction of underlying temperatures in reheating furnaces has the potential to reduce the overall heating time. We propose a Physics-Informed Neural Network (PINN) by incorporating prior physical knowledge using a set of novel Energy-Balance regularizers.
arXiv Detail & Related papers (2023-08-30T15:26:35Z)
Computation-efficient Deep Learning for Computer Vision: A Survey [121.84121397440337]
Deep learning models have reached or even exceeded human-level performance in a range of visual perception tasks. Deep learning models usually demand significant computational resources, leading to impractical power consumption, latency, or carbon emissions in real-world scenarios. New research focus is computationally efficient deep learning, which strives to achieve satisfactory performance while minimizing the computational cost during inference.
arXiv Detail & Related papers (2023-08-27T03:55:28Z)
Benchmarks and Custom Package for Energy Forecasting [55.460452605056894]
Energy forecasting aims to minimize the cost of subsequent tasks such as power grid dispatch. In this paper, we collected large-scale load datasets and released a new renewable energy dataset. We conducted extensive experiments with 21 forecasting methods in these energy datasets at different levels under 11 evaluation metrics.
arXiv Detail & Related papers (2023-07-14T06:50:02Z)
Energy Efficiency of Training Neural Network Architectures: An Empirical Study [11.325530936177493]
The evaluation of Deep Learning models has traditionally focused on criteria such as accuracy, F1 score, and related measures. The computations needed to train such models entail a large carbon footprint. We study the relations between DL model architectures and their environmental impact in terms of energy consumed and CO$$ emissions produced during training.
arXiv Detail & Related papers (2023-02-02T09:20:54Z)
Leveraging the structure of dynamical systems for data-driven modeling [111.45324708884813]
We consider the impact of the training set and its structure on the quality of the long-term prediction. We show how an informed design of the training set, based on invariants of the system and the structure of the underlying attractor, significantly improves the resulting models.
arXiv Detail & Related papers (2021-12-15T20:09:20Z)
Physics Informed Neural Networks for Control Oriented Thermal Modeling of Buildings [3.1132272756008375]
This paper presents a data-driven modeling approach for developing control-oriented thermal models of buildings. Along with measured data and building parameters, we encode the neural networks with the underlying physics that governs the thermal behavior of these buildings.
arXiv Detail & Related papers (2021-11-23T18:27:54Z)
On Energy-Based Models with Overparametrized Shallow Neural Networks [44.74000986284978]
Energy-based models (EBMs) are a powerful framework for generative modeling. In this work we focus on shallow neural networks. We show that models trained in the so-called "active" regime provide a statistical advantage over their associated "lazy" or kernel regime.
arXiv Detail & Related papers (2021-04-15T15:34:58Z)
Energy Drain of the Object Detection Processing Pipeline for Mobile Devices: Analysis and Implications [77.00418462388525]
This paper presents the first detailed experimental study of a mobile augmented reality (AR) client's energy consumption and the detection latency of executing Convolutional Neural Networks (CNN) based object detection. Our detailed measurements refine the energy analysis of mobile AR clients and reveal several interesting perspectives regarding the energy consumption of executing CNN-based object detection.
arXiv Detail & Related papers (2020-11-26T00:32:07Z)
AI Chiller: An Open IoT Cloud Based Machine Learning Framework for the Energy Saving of Building HVAC System via Big Data Analytics on the Fusion of BMS and Environmental Data [12.681421165031576]
Energy saving and carbon emission reduction in buildings is one of the key measures in combating climate change. The optimization of chiller system power consumption had been extensively studied in the mechanical engineering and building service domains. With the advance of big data and AI, the adoption of machine learning into the optimization problems becomes popular.
arXiv Detail & Related papers (2020-10-09T09:51:03Z)
NeurOpt: Neural network based optimization for building energy management and climate control [58.06411999767069]
We propose a data-driven control algorithm based on neural networks to reduce this cost of model identification. We validate our learning and control algorithms on a two-story building with ten independently controlled zones, located in Italy.
arXiv Detail & Related papers (2020-01-22T00:51:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.