A prediction model based on neural networks for the energy consumption of a bioclimatic building

Mena, R. A.; Rodríguez, Francisco; Castilla, M.; Arahal, Manuel R.

doi:10.1016/j.enbuild.2014.06.052

Cited by 144 publications

(80 citation statements)

References 32 publications

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“…Neural Network models take different inputs such as environmental parameters, occupancy information, inputs from the sensors on the HVAC system etc. to provide accurate load forecasts [34,35,37]. However, neural networks require significant amount of training data to produce such accurate results [34,36] and hence are not always suited for building load prediction, particularly in newly constructed buildings, where there is not much historical data available.…”

Section: Related Workmentioning

confidence: 99%

“…These models eliminate the need for extensive prior knowledge about the buildings and users; however, they often require a large amount of training data for each building of interest [34][35][36]39] or result in insufficient prediction accuracy, especially for long-term forecasting (1-5 day ahead forecasting) [37,40,43,44]. Among many, neural networks have been widely used in load forecasting [18,19,29,34,36,37] to obtain accurate predictions of building loads. Neural Network models take different inputs such as environmental parameters, occupancy information, inputs from the sensors on the HVAC system etc.…”

Section: Related Workmentioning

confidence: 99%

“…and with different input features-temperature, occupancy, weather parameters etc. [34][35][36][37][38][39][40][41][42]. These models eliminate the need for extensive prior knowledge about the buildings and users; however, they often require a large amount of training data for each building of interest [34][35][36]39] or result in insufficient prediction accuracy, especially for long-term forecasting (1-5 day ahead forecasting) [37,40,43,44].…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Robust Building Energy Load Forecasting Using Physically-Based Kernel Models

Prakash

Rajagopal

et al. 2018

Energies

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Robust and accurate building energy load forecasting is important for helping building managers and utilities to plan, budget, and strategize energy resources in advance. With recent prevalent adoption of smart-meters in buildings, a significant amount of building energy consumption data became available. Many studies have developed physics-based white box models and data-driven black box models to predict building energy consumption; however, they require extensive prior knowledge about building system, need a large set of training data, or lack robustness to different forecasting scenarios. In this paper, we introduce a new building energy forecasting method based on Gaussian Process Regression (GPR) that incorporates physical insights about load data characteristics to improve accuracy while reducing training requirements. The GPR is a non-parametric regression method that models the data as a joint Gaussian distribution with mean and covariance functions and forecast using the Bayesian updating. We model the covariance function of the GPR to reflect the data patterns in different forecasting horizon scenarios, as prior knowledge. Our method takes advantage of the modeling flexibility and computational efficiency of the GPR while benefiting from the physical insights to further improve the training efficiency and accuracy. We evaluate our method with three field datasets from two university campuses (Carnegie Mellon University and Stanford University) for both short-and long-term load forecasting. The results show that our method performs more accurately, especially when the training dataset is small, compared to other state-of-the-art forecasting models (up to 2.95 times smaller prediction error).

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Robust Building Energy Load Forecasting Using Physically-Based Kernel Models

Prakash

Rajagopal

et al. 2018

Energies

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“…Artificial neural networks (ANNs) play an important role in the forecasting of building energy consumption, and different kinds of ANNs have been given for this application. In [19], a short-term predictive ANN model for electricity demand was developed for the bioclimatic building. In [20], the Levenberg-Marquardt and Output-Weight-Optimization (OWO)-Newton algorithm-based ANN was utilized to forecast the residential building energy consumption.…”

Section: Introductionmentioning

confidence: 99%

Building Energy Consumption Prediction: An Extreme Deep Learning Approach

et al. 2017

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Building energy consumption prediction plays an important role in improving the energy utilization rate through helping building managers to make better decisions. However, as a result of randomness and noisy disturbance, it is not an easy task to realize accurate prediction of the building energy consumption. In order to obtain better building energy consumption prediction accuracy, an extreme deep learning approach is presented in this paper. The proposed approach combines stacked autoencoders (SAEs) with the extreme learning machine (ELM) to take advantage of their respective characteristics. In this proposed approach, the SAE is used to extract the building energy consumption features, while the ELM is utilized as a predictor to obtain accurate prediction results. To determine the input variables of the extreme deep learning model, the partial autocorrelation analysis method is adopted. Additionally, in order to examine the performances of the proposed approach, it is compared with some popular machine learning methods, such as the backward propagation neural network (BPNN), support vector regression (SVR), the generalized radial basis function neural network (GRBFNN) and multiple linear regression (MLR). Experimental results demonstrate that the proposed method has the best prediction performance in different cases of the building energy consumption.

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“…It can be divided into three aspects: environment, structure and operation process. For a given area of buildings, the main influence factors of the building energy consumption include regional climate characteristics, buildings, residential environment, building construction and operation management of heating system [2][3]. Because neural network [4] has strong nonlinear, parallel processing ability and robustness, and it does not need to set up complex mathematical model, so it has been favored by the researchers.…”

Section: Introductionmentioning

confidence: 99%

Application of GLBP Algorithm in the Prediction of Building Energy Consumption

Lv¹,

Zhong²,

Luo³

2015

ijacsa

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Abstract-Using BP neural network in past to predict the energy consumption of the building resulted in some shortcomings. Aiming at these shortages, a new algorithm which combined genetic algorithm with Levenberg-Marquardt algorithm (LM algorithm) was proposed. The proposed algorithm was used to improve the neural network and predict the energy consumption of buildings. First, genetic algorithm was used to optimize the weight and threshold of Artificial Neural Network (ANN). Levenberg-Marquardt algorithm was adopted to optimize the neural network training. Then the predicting model was set up in terms of the main effecting factors of the energy consumption. Furthermore, a public building power consumption data for one month is collected by establishing a monitoring platform to train and test the model. Eventually, the simulation result proved that the proposed model was qualified to predict short-term energy consumption accurately and efficiently.

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A prediction model based on neural networks for the energy consumption of a bioclimatic building

Cited by 144 publications

References 32 publications

Robust Building Energy Load Forecasting Using Physically-Based Kernel Models

Robust Building Energy Load Forecasting Using Physically-Based Kernel Models

Building Energy Consumption Prediction: An Extreme Deep Learning Approach

Application of GLBP Algorithm in the Prediction of Building Energy Consumption

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