Forecasting China's total energy demand and its structure using ADL-MIDAS model

He, Yongda; Lin, Boqiang

doi:10.1016/j.energy.2018.03.067

Cited by 66 publications

(34 citation statements)

References 46 publications

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“…In order to assign different weights according to the frequency, the weight function used in the MIDAS method was used in this study. In other MIDAS regression forecasting studies, by setting the temperature, the number of working days, the income variable, and the price variable as independent variables, the accuracy of short-term power demand forecasting could have been improved [26,27]. Saturdays were set to be half days, excluding holidays and Sundays, and add up the number of workdays [7].…”

Section: Midas Approach Methodsmentioning

confidence: 99%

“…Of these data, power demands from four institutions-residential, factory, hospital, and city hall-were used to calculate forecast accuracy for each power demand usage patterns. The collected data consist of 288 data per day every 5 min, enabling detailed power pattern analysis, unlike other future forecasting power usage studies, which used quarterly power usage data [27]. For each of the four facilities, we used different data components as shown in Table 1 for short-term, long-term, and seasonal forecasts.…”

Section: Datasetsmentioning

confidence: 99%

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Power Demand Forecasting using Long Short-Term Memory (LSTM) Deep-Learning Model for Monitoring Energy Sustainability

2020

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The purpose of this study is to design a novel custom power demand forecasting algorithm based on the LSTM Deep-Learning method regarding the recent power demand patterns. We performed tests to verify the error rates of the forecasting module, and to confirm the sudden change of power patterns in the actual power demand monitoring system. We collected the power usage data in every five-minute resolution in a day from some groups of the residential, public offices, hospitals, and industrial factories buildings in one year. In order to grasp the external factors and to predict the power demand of each facility, a comparative experiment was conducted in three ways; short-term, long-term, seasonal forecasting exp[eriments. The seasonal patterns of power demand usages were analyzed regarding the residential building. The overall error rates of power demand forecasting using the proposed LSTM module were reduced in terms of each facility. The predicted power demand data shows a certain pattern according to each facility. Especially, the forecasting difference of the residential seasonal forecasting pattern in summer and winter was very different from other seasons. It is possible to reduce unnecessary demand management costs by the designed accurate forecasting method.

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Section: Midas Approach Methodsmentioning

confidence: 99%

Section: Datasetsmentioning

confidence: 99%

Power Demand Forecasting using Long Short-Term Memory (LSTM) Deep-Learning Model for Monitoring Energy Sustainability

2020

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“…Despite increasing efforts laid on projecting China's future energy demand toward 2030, the existing studies have rarely focused on the central problem of the future energy consumption structure of production sectors. Some studies have attempted to project China's total energy demand or the demand for a specific energy product [16,17,24], yet few have projected the structure of the future energy demand [25][26][27][28]. Upon the same base year of 2016, studies agree that China's proportion of coal in energy consumption would fall rapidly to 55.2-60.0% by 2020 and 45.4-50.19% by 2030, and the proportion of oil would decline by 5.9-10.3 percent points by 2020 and 21.6-25.8 percent points by 2030.…”

Section: Introductionmentioning

confidence: 99%

Impacts of Clean Energy Substitution for Polluting Fossil-Fuels in Terminal Energy Consumption on the Economy and Environment in China

Chen

et al. 2019

Sustainability

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China has initiated various dedicated policies on clean energy substitution for polluting fossil-fuels since the early 2010s to alleviate severe carbon emissions and environmental pollution and accelerate clean energy transformation. Using the autoregressive integrated moving average (ARIMA) regression, we project the potentials of substituting coal and oil with clean energy for different production sectors in China toward the year 2030. Based on the projections, a dynamic multi-sectoral computable general equilibrium model, CHINAGEM, is employed to examine: the impacts of future clean energy substitution on China’s energy production, outputs of non-energy sectors, macro-economy, and CO2 emissions. First, we found that most production sectors are projected to replace polluting fossil-fuels with clean energy in their terminal energy consumption in 2017–2030. Second, clean energy substitution enables producing green co-benefits that would enable improvements in energy production structure, reductions in national CO2 emissions, and better real GDP and employment. Third, technological progress in non-fossil-fuel electricity could further benefit China’s clean and low-carbon energy transformation, accelerating the reduction in CO2 emissions and clean energy substitution. Furthermore, the most beneficiary are energy-intensive and high carbon-emission sectors owing to the drop in coal and oil prices, while the most negatively affected are the downstream sectors of electricity. Through research, various tentative improvement policies are recommended, including financial support, renewable electricity development, clean energy utilization technology, and clean coal technologies.

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“…It also helps to avoid imbalances in energy supply and demand and ensures energy security in the region and worldwide. In addition, adjustment and transformation of the energy structure directly affect environmental protection and governance in the region and worldwide [7]. In view of this, accurately forecasting primary energy demand and energy structure is important for energy planning.…”

Section: Introductionmentioning

confidence: 99%

A Novel Adaptive Intelligent Ensemble Model for Forecasting Primary Energy Demand

Zhao

Yao

et al. 2019

Energies

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Effectively forecasting energy demand and energy structure helps energy planning departments formulate energy development plans and react to the opportunities and challenges in changing energy demands. In view of the fact that the rolling grey model (RGM) can weaken the randomness of small samples and better present their characteristics, as well as support vector regression (SVR) having good generalization, we propose an ensemble model based on RGM and SVR. Then, the inertia weight of particle swarm optimization (PSO) is adjusted to improve the global search ability of PSO, and the improved PSO algorithm (APSO) is used to assign the adaptive weight to the ensemble model. Finally, in order to solve the problem of accurately predicting the time-series of primary energy consumption, an adaptive inertial weight ensemble model (APSO-RGM-SVR) based on RGM and SVR is constructed. The proposed model can show higher prediction accuracy and better generalization in theory. Experimental results also revealed outperformance of APSO-RGM-SVR compared to single models and unoptimized ensemble models by about 85% and 32%, respectively. In addition, this paper used this new model to forecast China’s primary energy demand and energy structure.

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Forecasting China's total energy demand and its structure using ADL-MIDAS model

Cited by 66 publications

References 46 publications

Power Demand Forecasting using Long Short-Term Memory (LSTM) Deep-Learning Model for Monitoring Energy Sustainability

Power Demand Forecasting using Long Short-Term Memory (LSTM) Deep-Learning Model for Monitoring Energy Sustainability

Impacts of Clean Energy Substitution for Polluting Fossil-Fuels in Terminal Energy Consumption on the Economy and Environment in China

A Novel Adaptive Intelligent Ensemble Model for Forecasting Primary Energy Demand

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