Electricity consumption probability density forecasting method based on LASSO-Quantile Regression Neural Network

You, He; Yang, Qin; Wang, Shuo; Wang, Xu; Wang, Chao

doi:10.1016/j.apenergy.2018.10.061

Cited by 130 publications

(50 citation statements)

References 45 publications

(42 reference statements)

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“…Classical deterministic theories are mainly applied to conduct the traditional short-term load forecasting. Such as time series method [3], back-propagation neural network (BPNN) model [4], gray model [5,6], and support vector regression [7][8][9], etc. Although these methods are widely adopted, there are still some outstanding problems, for example, (1) it is difficult to simulate the relationships between the variables affecting the electricity loads and the loads themselves by accurate mathematical model; (2) the forecasting accuracy requires improvements; (3) the forecasting effect is not satisfied; and (4) the real situation of the electricity load cannot be reflected in real time.…”

Section: Introductionmentioning

confidence: 99%

Application of the Weighted K-Nearest Neighbor Algorithm for Short-Term Load Forecasting

Fan¹,

Guo²,

Zheng³

et al. 2019

Energies

116

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In this paper, the historical power load data from the National Electricity Market (Australia) is used to analyze the characteristics and regulations of electricity (the average value of every eight hours). Then, considering the inverse of Euclidean distance as the weight, this paper proposes a novel short-term load forecasting model based on the weighted k-nearest neighbor algorithm to receive higher satisfied accuracy. In addition, the forecasting errors are compared with the back-propagation neural network model and the autoregressive moving average model. The comparison results demonstrate that the proposed forecasting model could reflect variation trend and has good fitting ability in short-term load forecasting.

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Section: Introductionmentioning

confidence: 99%

Application of the Weighted K-Nearest Neighbor Algorithm for Short-Term Load Forecasting

Fan¹,

Guo²,

Zheng³

et al. 2019

Energies

116

View full text Add to dashboard Cite

show abstract

“…Due to the nonlinearity, time-varying and uncertainty of, in recent years, the focus of electricity consumption forecasting research has shifted from the traditional methods that were mentioned above to emerging nonlinear methods. e common nonlinear methods that are used by scholars include the gray-forecasting model [13,24], the genetic algorithm [25,26], and the neural network model [27,28], in addition to more complex probability forecasting model [29,30] and the combination method [31,32]. In gray theory, we need to address the unknown gray information.…”

Section: Forecast Methods For Electricity Consumptionmentioning

confidence: 99%

Study of the Modified Logistic Model of Chinese Electricity Consumption Based on the Change of the GDP Growth Rate under the Economic New Normal

Cui

Zhao

2019

Mathematical Problems in Engineering

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In the context of the new normal, the global economy is entering a deep adjustment period, and the driving forces of development are also constantly changing. As a result, China’s economy has also entered a “new normal” phase in which it is growing in a manageable and relatively balanced manner. In addition, the new normal characteristics of the power industry’s development in China are also very significant, and they affect the adaptability of traditional power forecasting methods. By analyzing the new characteristics of China’s economic development and the changing electricity demand in recent years, this paper quantitatively studied the effect of the national economy on the consumption of electricity. Meanwhile, a modified logistic model based on the change of the gross domestic product (GDP) growth rate is constructed to make a reasonable prediction of the future power consumption in China. Subsequently, by calculating the elasticity of electricity consumption in the future, it is found that the coefficient decreases each year, which indicates that electricity consumption in China is following a new trend. Based on the research results, this paper proposes rational suggestions for China’s power development, and they are expected to provide references for the power planning and power industry layout in China.

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“…Yaoyao [17] presented a prediction method of consumption that combines the LASSO regressions with the Quantile Regression Neural Networks (LASSO-QRNN). The LASSO regression is used to produces high-quality attributes and to reduce the data dimensionality effectively.…”

Section: Related Workmentioning

confidence: 99%

An Artificial Neural Network Approach to Forecast the Environmental Impact of Data Centers

et al. 2019

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Due to the high demands of new technologies such as social networks, e-commerce and cloud computing, more energy is being consumed in order to store all the data produced and provide the high availability required. Over the years, this increase in energy consumption has brought about a rise in both the environmental impacts and operational costs. Some companies have adopted the concept of a green data center, which is related to electricity consumption and CO2 emissions, according to the utility power source adopted. In Brazil, almost 70% of electrical power is derived from clean electricity generation, whereas in China 65% of generated electricity comes from coal. In addition, the value per kWh in the US is much lower than in other countries surveyed. In the present work, we conducted an integrated evaluation of costs and CO2 emissions of the electrical infrastructure in data centers, considering the different energy sources adopted by each country. We used a multi-layered artificial neural network, which could forecast consumption over the following months, based on the energy consumption history of the data center. All these features were supported by a tool, the applicability of which was demonstrated through a case study that computed the CO2 emissions and operational costs of a data center using the energy mix adopted in Brazil, China, Germany and the US. China presented the highest CO2 emissions, with 41,445 tons per year in 2014, followed by the US and Germany, with 37,177 and 35,883, respectively. Brazil, with 8459 tons, proved to be the cleanest. Additionally, this study also estimated the operational costs assuming that the same data center consumes energy as if it were in China, Germany and Brazil. China presented the highest kWh/year. Therefore, the best choice according to operational costs, considering the price of energy per kWh, is the US and the worst is China. Considering both operational costs and CO2 emissions, Brazil would be the best option.

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Electricity consumption probability density forecasting method based on LASSO-Quantile Regression Neural Network

Cited by 130 publications

References 45 publications

Application of the Weighted K-Nearest Neighbor Algorithm for Short-Term Load Forecasting

Application of the Weighted K-Nearest Neighbor Algorithm for Short-Term Load Forecasting

Study of the Modified Logistic Model of Chinese Electricity Consumption Based on the Change of the GDP Growth Rate under the Economic New Normal

An Artificial Neural Network Approach to Forecast the Environmental Impact of Data Centers

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