Using BP nerual networks for the simulation of energy consumption

Li, Jianqiang; Yu, Lu

doi:10.1109/smc.2014.6974479

Cited by 4 publications

(4 citation statements)

References 4 publications

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“…The frequency of the clock is set to 250MHz, and the first ten pixels of the input image are set to 1,2,3,4,5,6,7,8,9,10; then the rest pixels of the image are all set to 0. The first ten elements of the convolution kernel are all set to 1, the rest are all set to 0.…”

Section: Experiments Simulation Resultsmentioning

confidence: 99%

“…It designs the multi-layer neuron model by imitating the biological (human) brain which recognizes objects by vision, so that the computer vision recognition system can improve recognition rate, and achieve strong resistance to displacement and deformation interference [1] . The convolution neural network [2] algorithm is a new algorithm of computer vision field.…”

Section: Background Introductionmentioning

confidence: 99%

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A Hardware Design of High-throughput and Resource-saving Image Convolution

Shu¹,

Peng²,

Chen³

et al. 2017

dtcse

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Abstract. This paper proposes a hardware design of high-throughput and resource-saving image convolution. The key part of the design is consist of two aspects, buffer and computing part. For the buffer part, serial shifting register chain has been applied to buffer some of the pixels so that large number of buffer resources could be saved, thus realizing data multiplex; as for computing part, logic resource consumed is not much but time consumed for the serial convolution operation is too long. Since buffer part consumes little resource, logic duplication can be used to realize parallel design, thus accelerating the convolution operation, which reduces the time consumed largely and makes the throughput higher. In one word, this design not only accelerates the convolution operation, but also reduces the consumed resources.

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Section: Experiments Simulation Resultsmentioning

confidence: 99%

Section: Background Introductionmentioning

confidence: 99%

A Hardware Design of High-throughput and Resource-saving Image Convolution

Shu¹,

Peng²,

Chen³

et al. 2017

dtcse

View full text Add to dashboard Cite

show abstract

“…Therefore, it is reasonable to establish the building energy consumption analysis model based on neural network and other algorithms. Considering the advantages of building energy-saving technology and neural network, taking the average monthly temperature, building surface and the proportion of non winter and summer vacation as the input of the model and building energy as the output of the model, the energy consumption analysis model scheme of campus public buildings designed in this paper is feasible [10,11].…”

Section: Feasibility Analysis Of Building Energy Consumption Analysis...mentioning

confidence: 99%

Prediction of Low-Energy Building Energy Consumption Based on Genetic BP Algorithm

Lü¹,

Gong²,

Kipnis³

2022

Computers, Materials &Amp; Continua

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Combined with the energy consumption data of individual buildings in the logistics group of Yangtze University, the analysis model scheme of energy consumption of individual buildings in the university is studied by using Back Propagation (BP) neural network to solve nonlinear problems and have the ability of global approximation and generalization. By analyzing the influence of different uses, different building surfaces and different energysaving schemes on the change of building energy consumption, the grey correlation method is used to determine the main influencing factors affecting each building energy consumption, including uses, building surfaces and energy-saving schemes, which are used as the input of the model and the building energy consumption as the output of the model, so as to establish the building energy consumption analysis model based on BP neural network. However, in practical application, BP neural network has the defects of slow convergence and easy to fall into local minima. In view of this, this paper uses genetic algorithm to optimize the weight and threshold of BP neural network, completes the improvement of various building energy consumption analysis models, and realizes the qualitative analysis of building energy consumption. The model verification results show that the viscosity of the building energy consumption analysis model based on genetic algorithm improved BP neural network algorithm (GABP) in this paper is relatively high, which is more accurate than the results of the traditional BP neural network model, and the relative error of the analysis model is reduced from 11.56% to 8.13%, which proves that the GABP can be better suitable for the study of school building energy consumption analysis model, It is applied to the prediction of building energy consumption, which lays a foundation for the realization of carbon neutralization in the South expansion plan of Yangtze University.

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“…The nonlinear prediction models developed by scholars and put into application are as follows. The BP neural network model [30][31][32] can clearly reflect the relationship between the influencing factors. It has been widely used in the forecasting field and has a high degree of accuracy.…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting China’s Coal Power Installed Capacity: A Comparison of MGM, ARIMA, GM-ARIMA, and NMGM Models

Yang

2018

Sustainability

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Construction of new coal-fired power plants in China has posed a huge challenge to energy sustainability. Forecasting the installed capacity more accurately can serve to develop better energy sustainability strategy. A comparison between linear and non-linear forecasting models can more comprehensively describe the characteristics of the prediction data and provide multi-angle analysis of the prediction results. In this paper, we develop four time-series forecasting techniques-metabolism grey model (MGM), autoregressive integrated moving average (ARIMA), grey model (GM)-ARIAM, and nonlinear metabolism grey model (NMGM)-for better forecasting of coal-fired power installed capacity. The average relative errors between the simulation and actual data of the MGM, GM-ARIMA, ARIMA, and NMGM model are 3.37%, 2.13%, 3.71% and 2.36% respectively, which indicate those four models can produce highly accurate results. The forecasting results show the average annual growth rate of China's coal-fired power installed capacity in the next ten years (2017-2016) will be 5.26% a year, which is slower than the average annual growth rate (8.20% a year) for 2007-2016. However, the average annual new added installed capacity for 2017-2026 will be 74 gigawatts, which is higher than the average annual added installed capacity (56 gigawatts) for 2007-2016.

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Using BP nerual networks for the simulation of energy consumption

Cited by 4 publications

References 4 publications

A Hardware Design of High-throughput and Resource-saving Image Convolution

A Hardware Design of High-throughput and Resource-saving Image Convolution

Prediction of Low-Energy Building Energy Consumption Based on Genetic BP Algorithm

Forecasting China’s Coal Power Installed Capacity: A Comparison of MGM, ARIMA, GM-ARIMA, and NMGM Models

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