Electricity consumption prediction using a neural-network-based grey forecasting approach

Hu, Yi‐Chung

doi:10.1057/s41274-016-0150-y

Cited by 103 publications

(44 citation statements)

References 27 publications

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“…This sort of data can be further used in different machine-learning approaches to train systems in order to predict future energy consumption. The importance of building energy consumption predictions in order to make the most informed real-time decisions has been highlighted in many recent studies; and some contributions have come up with prediction models through data-driven or machine-learning approaches [9,10]. According to [11], these predictions are equally important and effective for both the user of a residential building and power-generating companies.…”

Section: Introductionmentioning

confidence: 99%

Prediction-Learning Algorithm for Efficient Energy Consumption in Smart Buildings Based on Particle Regeneration and Velocity Boost in Particle Swarm Optimization Neural Networks

Malik

Kim

2018

Energies

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Electricity, the most important form of energy and an indispensable resource, primarily for commercial and residential smart buildings, faces challenges requiring its hyper efficient consumption and production. Therefore, accurate energy consumption predictions are required in order to manage and optimize the energy consumption of smart buildings. Many studies have taken advantage of the power and robustness of neural networks (NN) when it comes to accurate predictions. A few studies have also used the particle swarm optimization (PSO) algorithm along with NNs to enhance and optimize the predictions. In this work, we study prediction learning using PSO-based neural networks (PSO-NN) and propose modifications in order to increase prediction accuracy. Our proposed modifications are re-generation based PSO-NN (R-PSO-NN) and velocity boost-based PSO-NN (VB-PSO-NN). The performance metrics used are: prediction accuracy, number of particles used, and number of epochs required. We compare the results of NN, PSO-NN, R-PSO-NN and VB-PSO-NN based on the performance metrics.

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Section: Introductionmentioning

confidence: 99%

Prediction-Learning Algorithm for Efficient Energy Consumption in Smart Buildings Based on Particle Regeneration and Velocity Boost in Particle Swarm Optimization Neural Networks

Malik

Kim

2018

Energies

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“…Then Yi-Chung Hu et al applied a neural network-based grey model NNGM (1,1) to study and forecasted the electricity demand in Turkey. The MAPE value of the total electricity demand in Turkey was 3.41% [51]. Furthermore, Xu Ning improved the grey model and obtained the new model IRGM (1,1).…”

Section: Overview Of Forecasting Methodsmentioning

confidence: 99%

Forecasting India’s Electricity Demand Using a Range of Probabilistic Methods

Zhou

2019

Energies

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With serious energy poverty, especially concerning power shortages, the economic development of India has been severely restricted. To some extent, power exploitation can effectively alleviate the shortage of energy in India. Thus, it is significant to balance the relationship between power supply and demand, and further stabilize the two in a reasonable scope. To achieve balance, a prediction of electricity generation in India is required. Thus, in this study, five methods, the metabolism grey model, autoregressive integrated moving average, metabolic grey model-auto regressive integrated moving average model, non-linear metabolic grey model and non-linear metabolic grey model-auto regressive integrated moving average model, are applied. We combine the characteristics of linear and nonlinear models, making a prediction and comparison of Indian power generation. In this way, we enrich methods for prediction research on electrical energy, which avoids large errors in trends of electricity generation due to those accidental factors when a single predictive model is used. In terms of prediction outcomes, the average relative errors from five models above are 1.67%, 1.62%, 0.84%, 1.84%, and 1.37%, respectively, which indicates high accuracy and reference value of these methods. In conclusion, India’s power generation will continue to grow with an average annual growth rate of 5.17% in the next five years (2018–2022).

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“…Nine evaluation criteria tabulated in Table 2 are adopted to evaluate the prediction ability of the aforementioned grey prediction models. Meanwhile, Lewis' criteria [47]shown in Table 3 are also adopted to illustrate the prediction power of grey models.…”

Section: Contrast Grey Models and Performance Criteriamentioning

confidence: 99%

A Novel Power‐Driven Grey Model with Whale Optimization Algorithm and Its Application in Forecasting the Residential Energy Consumption in China

Zhang

She

2019

Complexity

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Along with the improvement of Chinese people’s living standard, the proportion of residential energy consumption in total energy consumption is rapidly increasing in China year by year. Accurately forecasting the residential energy consumption is conducive to making energy programming and supply plan for the administrative departments or energy companies. By improving the grey action quantity of traditional grey model with an exponential time term, a novel power-driven grey model is proposed to forecast energy consumption as reference data for decision makers. The nonlinear parameter of power-driven grey action quantity is a crucial factor to influence the prediction precision. To promote the prediction accuracy of the power-driven grey model, whale optimization algorithm is adopted to seek for the optimal value of the nonlinear parameter. Two validations on real-world datasets are conducted, and the results indicate that the power-driven grey model has significant advantages on the aspect of prediction performance compared with the other seven classical grey prediction methods. Finally, the power-driven grey model is applied in forecasting the total residential energy and the thermal energy consumption of China.

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Electricity consumption prediction using a neural-network-based grey forecasting approach

Cited by 103 publications

References 27 publications

Prediction-Learning Algorithm for Efficient Energy Consumption in Smart Buildings Based on Particle Regeneration and Velocity Boost in Particle Swarm Optimization Neural Networks

Prediction-Learning Algorithm for Efficient Energy Consumption in Smart Buildings Based on Particle Regeneration and Velocity Boost in Particle Swarm Optimization Neural Networks

Forecasting India’s Electricity Demand Using a Range of Probabilistic Methods

A Novel Power‐Driven Grey Model with Whale Optimization Algorithm and Its Application in Forecasting the Residential Energy Consumption in China

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