A neural network-based multi-zone modelling approach for predictive control system design in commercial buildings

Hao, Huang; Chen, Lei; Hu, Eric

doi:10.1016/j.enbuild.2015.03.045

Cited by 113 publications

(85 citation statements)

References 31 publications

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“…A kind of switched uncertain nonlinear system was controlled by an adaptive fuzzy tracking control method in [21]. In [22], multi-zone buildings were identified by an artificial neural network and controlled by predictive control techniques. The robust H  finite-time control method for a discrete system was discussed in [23].…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Predictive Control of a Hydropower System Model

Zhang

Chen

2015

Entropy

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A six-dimensional nonlinear hydropower system controlled by a nonlinear predictive control method is presented in this paper. In terms of the nonlinear predictive control method; the performance index with terminal penalty function is selected. A simple method to find an appropriate terminal penalty function is introduced and its effectiveness is proved. The input-to-state-stability of the controlled system is proved by using the Lyapunov function. Subsequently a six-dimensional model of the hydropower system is presented in the paper. Different with other hydropower system models; the above model includes the hydro-turbine system; the penstock system; the generator system; and the hydraulic servo system accurately describing the operational process of a hydropower plant. Furthermore, the numerical experiments show that the six-dimensional nonlinear hydropower system controlled by the method is stable. In addition, the numerical experiment also illustrates that the nonlinear predictive control method enjoys great advantages over a traditional control method in nonlinear systems. Finally, a strategy to combine the nonlinear predictive control method with other methods is proposed to further facilitate the application of the nonlinear predictive control method into practice.

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Section: Introductionmentioning

confidence: 99%

Nonlinear Predictive Control of a Hydropower System Model

Zhang

Chen

2015

Entropy

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“…MATLAB (Matrix Laboratory) [30] and its neural network toolbox were used to develop the initial ANN model. The input variables for the prediction of the output variable, which is the amount of cooling energy consumption during the setback period (ENSETBACK, kWh), were composed of the setback temperature (TEMPSETBACK, °C), outdoor air temperature (TEMPOUT, °C), average outdoor air temperature from an hour earlier to the last control cycle (TEMPOUT, AVE, nStep-60~nStep-1, °C), average outdoor air temperature from two hours earlier to an hour earlier (TEMPOUT, AVE, nStep-120~nStep-61, °C), average outdoor air temperature from three hours earlier to two hours earlier (TEMPOUT, AVE, nStep-180~nStep-121, °C), average outdoor air temperature from four hours earlier to three hours earlier (TEMPOUT, AVE, nStep-240~nStep-181, °C), average outdoor air temperature from five hours earlier to four hours earlier (TEMPOUT, AVE, nStep-300~nStep-241, °C), average outdoor air temperature from six hours earlier to five hours earlier (TEMPOUT, AVE, nStep-360~nStep-301, °C), and daytime setback period (ENSETBACK, minutes).…”

Section: Development Of the Initial Modelmentioning

confidence: 99%

“…reduced overcooling and overheating, and the amount of energy consumption of the heating and cooling systems was significantly reduced [20][21][22][23][24][25][26][27][28][29][30][31][32].…”

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confidence: 99%

Prediction Performance of an Artificial Neural Network Model for the Amount of Cooling Energy Consumption in Hotel Rooms

et al. 2015

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This study was conducted to develop an artificial neural network (ANN)-based prediction model that can calculate the amount of cooling energy during the setback period of accommodation buildings. By comparing the amount of energy needed for diverse setback temperatures, the most energy-efficient optimal setback temperature could be found and applied in the thermal control logic. Three major processes that used the numerical simulation method were conducted for the development and optimization of an ANN model and for the testing of its prediction performance, respectively. First, the structure and learning method of the initial ANN model was determined to predict the amount of cooling energy consumption during the setback period. Then, the initial structure and learning methods of the ANN model were optimized using parametrical analysis to compare its prediction accuracy levels. Finally, the performance tests of the optimized model proved its prediction accuracy with the lower coefficient of variation of the root mean square errors (CVRMSEs) of the simulated results and the predicted results under generally accepted levels. In conclusion, the proposed ANN model proved its potential to be applied to the thermal control logic for setting up the most energy-efficient setback temperature. OPEN ACCESSEnergies 2015, 8 8227

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“…This kind of data collection would be very time consuming and impractical in case of hundreds or thousands of buildings. Other ways to get a representative data sets for the model identification include the use of data with minimum and maximum historical records [21] or collecting data by performing step response tests [46]. Also, many of the reported studies have acquired data from a particular test building for the model identification [47][48][49][50][51][52].…”

Section: Introductionmentioning

confidence: 99%

“…Models based on physical principles can vary largely from extremely complex to more simplified structures with respect to the number of parameters and variables [14][15][16][17] but they usually need detailed information on the building characteristics and are in general too computationally heavy to be effectively used for control purposes. On the other hand, data driven models [18][19][20][21][22][23][24][25][26][27][28] are based solely on measurements and are typically identified without information on the physical nature of the building properties. Hybrid or grey box models are a combination of data driven and physical modelling approaches [29][30][31][32][33][34][35].…”

Section: Introductionmentioning

confidence: 99%

A Dynamic Model for Indoor Temperature Prediction in Buildings

2018

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A novel dynamic model for the temperature inside buildings is presented, aiming to improve energy efficiency by providing predictive information on the heat demand. To analyse the performance and generalizability of the modelling approach, real measurement data was gathered from five different types of buildings. Easily available data from various sources was utilized. The chosen model structure leads to a minimal number of input variables and free parameters. Simulations with real data from five buildings, and applying the identical model structure showed that the average modelling error during the 28-h prediction horizon was constantly below 5%. The results thus demonstrate that the model structure can be standardized and easily applied to predict the indoor temperatures of large buildings. This would finally enable demand side management and the predictive optimization of the heat demand at city level.

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A neural network-based multi-zone modelling approach for predictive control system design in commercial buildings

Cited by 113 publications

References 31 publications

Nonlinear Predictive Control of a Hydropower System Model

Nonlinear Predictive Control of a Hydropower System Model

Prediction Performance of an Artificial Neural Network Model for the Amount of Cooling Energy Consumption in Hotel Rooms

A Dynamic Model for Indoor Temperature Prediction in Buildings

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