Operational supply and demand optimisation of a multi-vector district energy system using artificial neural networks and a genetic algorithm

Reynolds, Jonathan; Ahmad, Muhammad Waseem; Rezgui, Yacine; Hippolyte, Jean-Laurent

doi:10.1016/j.apenergy.2018.11.001

Cited by 113 publications

(54 citation statements)

References 35 publications

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“…The genetic algorithm (GA) was adopted to realize the equipment capacity configuration in the first layer, and the optimization solution toolbox of Python was used to achieve the optimal dispatching of the equipment in the second layer. Reynolds et al [31] developed an optimized dispatching management method, in which the artificial neural network (ANN) and the GA were adopted to predict the output of renewable energy equipment and determine the operation strategy of the equipment, respectively. In these studies, however, the strategy used at the operational stage was not combined with the selection of energy conversion technologies at the design stage, and the safety of the device pre-start and reliability of the system were not considered.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Therefore, the two following constraints could be used to ensure the reliability of switching: First, the configuration capacity of CCP is increased to provide sufficient power for the IT equipment and chillers during the pre-cooling including IT, chiller, etc. Equations (30) and (31) can be brought into the configuration model as a constraint to avoid switching faults. Clearly, the above switching logic is based on the fact that the chillers can meet the total cooling load solely.…”

Section: Switching Logic Analysismentioning

confidence: 99%

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Configuration Optimization Model for Data-Center-Park-Integrated Energy Systems under Economic, Reliability, and Environmental Considerations

Liu

et al. 2020

Energies

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The analysis of energy configuration in the planning of data-center-park-integrated energy systems (DCP-IESs) has become an enormous challenge, owing to multi-energy complementarity, energy cascade use, and energy security. In this study, a configuration model of DCP-IESs was established to obtain the economic and low-carbon energy uses of the data centers, based on mixed integer linear programming. In the model, carbon emissions were converted to economic indicators through carbon pricing. Then, the configuration model was modified according to the security of the proposed device switching logic, and the Markov-based reliability estimation method was used to ensure the redundant design of the configuration. Using the new energy configuration method, the DCP-IES configuration scheme could be obtained under economical, low-carbon, and high reliability conditions. A data center park in Shanghai was selected as a case study, and the results are as follows: it will only take 2.88 years for the economics of DCP-IES to reach those of traditional data center energy systems. Additionally, the use of configuration model in DCP-IES would result in a reduction in annual carbon emissions of 39,323 tons, with a power usage effectiveness of 1.388, whereas an increase in reliability results in an increasingly faster increase in the initial investment cost.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Switching Logic Analysismentioning

confidence: 99%

Configuration Optimization Model for Data-Center-Park-Integrated Energy Systems under Economic, Reliability, and Environmental Considerations

Liu

et al. 2020

Energies

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“…For the former approach, various operating techniques using a DNN have been studied as methods for efficient HVAC system control. Jonathan et al [6] used an artificial neural network to predict energy use and optimize the energy supply as an optimization method. Kato et al [10] proposed a thermal load prediction technique that was more efficient than the Multi-Layer Perceptron (MLP) method by applying three hidden layers in a Recurrent Neural Network (RNN) approach.…”

Section: Related Workmentioning

confidence: 99%

“…In recent years, studies have been actively attempting to integrate artificial intelligence technology with such systems to improve the intelligence and utilization of sensors. In particular, research is underway to reduce energy by forecasting the energy demand and supply [6], designing automatic fault detection technology using deep learning methods [5,7], and developing smart sensors with artificial intelligence (AI) [8]. Because the data used to train the AI come from the sensors, they are essential for enhancing the intelligence of buildings.…”

Section: Introductionmentioning

confidence: 99%

Sensorless Air Flow Control in an HVAC System through Deep Learning

Son

Kim

2019

Applied Sciences

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Sensor-based intelligence is essential in future smart buildings, but the benefits of increasing the number of sensors come at a cost. First, purchasing the sensors themselves can incur non-negligible costs. Second, since the sensors need to be physically connected and integrated into the heating, ventilation, and air conditioning (HVAC) system, the complexity and the operating cost of the system are increased. Third, sensors require maintenance at additional costs. Therefore, we need to pursue the appropriate technology (AT) in terms of the number of sensors used. In the ideal scenario, we can remove excessive sensors and yet achieve the intelligence that is required to operate the HVAC system. In this paper, we propose a method to replace the static pressure sensor that is essential for the operation of the HVAC system through the deep neural network (DNN).

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“…They achieve high accuracy, are computationally efficient, and require no knowledge of the physical relationships between inputs and outputs. ANNs are a powerful tool for making predictions based on a large number of interrelated experimental data [5,6,21,27,[40][41][42][43][54][55][56].…”

Section: Introductionmentioning

confidence: 99%

The Use of Artificial Neural Networks for Forecasting of Air Temperature inside a Heated Foil Tunnel

Francik

Kurpaska

2020

Sensors

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It is important to correctly predict the microclimate of a greenhouse for control and crop management purposes. Accurately forecasting temperatures in greenhouses has been a focus of research because internal temperature is one of the most important factors influencing crop growth. Artificial Neural Networks (ANNs) are a powerful tool for making forecasts. The purpose of our research was elaboration of a model that would allow to forecast changes in temperatures inside the heated foil tunnel using ANNs. Experimental research has been carried out in a heated foil tunnel situated on the property of the Agricultural University of Krakow. Obtained results have served as data for ANNs. Conducted research confirmed the usefulness of ANNs as tools for making internal temperature forecasts. From all tested networks, the best is the three-layer Perceptron type network with 10 neurons in the hidden layer. This network has 40 inputs and one output (the forecasted internal temperature). As the networks input previous historical internal temperature, external temperature, sun radiation intensity, wind speed and the hour of making a forecast were used. These ANNs had the lowest Root Mean Square Error (RMSE) value for the testing data set (RMSE value = 3.7 °C).

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Operational supply and demand optimisation of a multi-vector district energy system using artificial neural networks and a genetic algorithm

Cited by 113 publications

References 35 publications

Configuration Optimization Model for Data-Center-Park-Integrated Energy Systems under Economic, Reliability, and Environmental Considerations

Configuration Optimization Model for Data-Center-Park-Integrated Energy Systems under Economic, Reliability, and Environmental Considerations

Sensorless Air Flow Control in an HVAC System through Deep Learning

The Use of Artificial Neural Networks for Forecasting of Air Temperature inside a Heated Foil Tunnel

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