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Wind power forecasting can effectively improve the energy utilization efficiency of a power system and ensure its stable operation. In this study, a novel hybrid multistep prediction model, including the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), variational mode decomposition (VMD), the kernel principal component analysis (KPCA), an enhanced arithmetic optimization algorithm (ENAOA), a bidirectional long short-term memory (BILSTM) neural network, and error correction, was designed for short-term wind power forecasting. First, the collected original wind power data were decomposed into multiple intrinsic mode functions (IMFs) through a secondary decomposition composed of the CEEMDAN and VMD, which eliminated the interactions between different components to achieve denoising. Second, the KPCA was adopted to reduce the dimensionality of the multiple IMFs by extracting the principal components, effectively reducing the complexity of the multidimensional IMF data and improving the forecasting efficiency of the proposed prediction model. Subsequently, an ENAOA was proposed based on the Sobol sequence, adaptive T-distribution, and random walk strategy to optimize the BILSTM parameters. Finally, multiple preprocessed components were predicted by the optimized BILSTM, after which error correction was performed to obtain the final prediction results, which could further reduce the forecast error of the designed prediction model. Based on two sets of data collected from a wind farm in northwest China, the simulation results of 1-step, 4-step, 7-step, and 10-step forecasting revealed that compared with other incomplete models, the various algorithms adopted in the hybrid forecasting model reduced the prediction errors to different degrees, significantly enhanced the wind power prediction performance, and validated the effectiveness and feasibility of the proposed model. • Original wind power data is preprocessed through CEEMNAN and VMD. • KPCA is adopted to extract principal components of several IMFs. • Propose an improved ENAOA optimization algorithm through three improvements. • Error correction is employed to further increase the forecast accuracy. • The hybrid multi-step forecasting model greatly reduces the prediction error. [ABSTRACT FROM AUTHOR]

Accurate prognostic for the remaining useful life (RUL) of lithium-ion batteries (LIBs) is extremely crucial to the stable operation and timely maintenance of a battery system. Nevertheless, battery lifespan is difficult to measure due to the capacity regeneration in non-linear and unstable degradation trend. To increase the prediction accuracy, the Time Varying Filter-based Empirical Mode Decomposition (TVF-EMD) is innovatively introduced to decompose the original capacity data into subseries. Meanwhile, the complexities of the subseries are measured by the Box-counting dimension (BCD). Moreover, Fast Ensemble Empirical Mode Decomposition (FEEMD) is exploited to further decompose the most complex subseries. Additionally, Bidirectional Gated Recurrent Unit (BiGRU) is established for (sub-)subseries prognosis. The prediction performance is further strengthened by an error correction method (ECM). Eventually, the effectiveness of the proposed prognosis framework is verified on two battery datasets. The experimental results illustrate that the maximum root mean squared error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of the proposed hybrid framework are merely 1.917, 0.434 and 0.706% respectively. Compared with two decomposition methods, MAE can be reduced by at least 22.73%, and a reduction of not less than 7.4% in RMSE is achieved. • A new hybrid methodis proposed for the remaining useful life forecasting. • The secondary decomposition method employed can enhance the composition ability. • The box-counting dimension is utilized to choose the most complicated sub-layers. • An error correction scheme is proposed to further enhance prediction performance. [ABSTRACT FROM AUTHOR]

Forecasting international crude oil is a well-known issue. The hybrid modeling principle tells us that combining different methods could take full advantage of all the merits and leave out the shortcomings. Therefore, hybrid methodology has been widely used in current research. In this study, a novel decompose-ensemble prediction process combining the ensemble empirical mode decomposition (EEMD) and artificial neural network (ANN) is proposed. Moreover, this method, i.e., EEMD-ANN-ADD method, adds the decompose-ensemble to the single AI model to further improve the predicting accuracy. The overall process can be divided into four steps: model selection via Akaike's information criterion (AIC), data decomposition via EEMD, individual prediction via ANN and ensemble prediction through addition ensemble method. To verify the results, we use the official data of oil price to conduct the predicting. The result confirms that “decompose-ensemble” models are better than the normal hybrid one, in terms of prediction accuracy (both level and directional measurement) and modified Diebold-Mariano test. What's more, back to the decompose-ensemble models, the EEMD-based one outperforms the empirical mode decomposition (EMD) one. At last but not the least, AIC gives us reasonable and convincing statement about determining the value of lag. Generally speaking, this novel forecasting technique is a prominent insight for the price of crude oil. [ABSTRACT FROM AUTHOR]

This paper analyzed the energy saving potentials of China's agricultural sector by using an econometric approach and a scenario analysis. First a co-integration analysis and an error correction model are employed to analyze the long-term equilibrium relationship between agricultural energy consumption and its influencing factors such as agricultural output, mechanical power, agricultural industrial structure, fiscal expenditure and energy prices during the period 1980–2012. Then stability test, fitting effect test and Monte Carlo simulation method are applied to confirm the rationality of the prediction model. Further, the scenario analysis method is used to predict the energy-saving potentials in 2020 and 2025 under different scenarios. It is found that agricultural output and mechanical power have positive impacts on energy consumption, while agricultural industrial structure, fiscal expenditure and energy prices have negative influences. The results demonstrate that under BAU condition, the energy demand of China's agricultural sector will reach 128, 94 and 161,61million tons of standard coal by 2020 and 2025 respectively. Moreover, the energy savings potential will be 7, 967 million tons and 15,701 million tons under moderate and advanced scenarios by the year 2020, and 17, 225 million tons and 31,094 million tons by the year 2025. This study provides a reference for establishing energy saving policies for China's agricultural sector. [ABSTRACT FROM AUTHOR]

The effective utilization of wind energy is a positive development trend in renewable energy that provides an effective impetus to alleviate global warming. However, the complex volatility and extreme suddenness of wind speeds make it difficult to accurately predict wind speeds. To overcome this challenge, a novel short-term wind speed prediction model based on double decomposition, piecewise error correction, Elman neural network and the autoregressive integrated moving average model is proposed. In this model, the fuzzy entropy is applied to judge the complexity of each subseries generated by variational mode decomposition. To address error correction, a piecewise error correction method that needs to extract only a part of the error sequence as a training set is proposed. Three experiments utilizing 6 Datasets and 14 compared models are conducted to verify the effectiveness of the proposed model. The results show that (i) the hybrid strategy based on double decomposition and piecewise error correction can improve the final prediction accuracy of different single models; (ii) the proposed hybrid model can effectively handle both linear and nonlinear problems; (iii) the root mean squared error of the proposed model for Dataset1-6 in seven-step forecasting are 0.1243, 0.1444, 0.0233, 0.1015, 0.1846 and 0.2513, respectively. • The double decomposition strategy is applied to wind speed prediction. • A novel piecewise error correction method has been developed. • Fuzzy entropy is used to judge the complexity of subsequences. • The proposed model is verified using 6 wind speed datasets in multi-step forecasting. [ABSTRACT FROM AUTHOR]

Abstract: In this paper we investigate the co-movement and the causality relationship between energy consumption as well as electricity consumption and the HDI (human development index) using as a proxy of human well-being and by including energy prices as an additional variable, in fifteen developing countries for the period 1988 to 2008. Recently developed tests for the panel unit root, heterogeneous panel cointegration, and panel-based error correction models are employed. The empirical results support the neutrality hypothesis in the short-term, regards total energy or electricity consumption, implying an absence of causality running in either direction. In the short term, energy as well as electricity consumption has a neutral effect on the HDI. In the long-term the findings provide a clear support of a negative cointegration relationship between energy consumption and the HDI. While a positive cointegration relationship exists between electricity consumption and HDI. A 1% increase in per capita energy consumption reduces the HDI by 0.8% and, a 1% increase in per capita electricity consumption increases the HDI by 0.22%. Moreover, a 1% increase in energy price reduces the HDI by around 0.11%. This study thus provides empirical evidence of long-run and causal relationships between energy consumption and the HDI for our sample of countries; supporting the assertion that lack or limited access to modern energy services could hamper economic and human development prospects of countries and underpins all the MDGs (millennium development goals). [Copyright &y& Elsevier]

Considering the significance of renewable energy in the sustainable energy future discussion, it is important to understand its influencing factors in order to draw result implications for policy formulation. This study examines these influencing factors for Ghana in the context of an unimpressive renewable electricity scale-up amid the potentials and commitment. It proceeds to forecast the trend of renewable energy fifteen years after the sample period. The results, based on the application of the Vector Error Correction Model and Johansen cointegration technique, on a dataset covering 1980 to 2015, shows that Ghana's renewable electricity is mainly driven by foreign direct investment and trade openness, with real GDP per capita being inconsequential. What's more, both financial development and fossil fuel consumption undermine renewable energy development in Ghana. The dynamic forecast reveals an uninspiring future for renewable electricity, as generation will continue to plummet if conditions remain the same. In light of the findings, we recommend determinant-based policies anchored on the intentional handling of the appraised factors. One key contribution of this study is embodied in the fact that it makes the first empirical attempt in examining the determinants of renewable energy in Ghana. • The influencing factors of renewable energy is examined for Ghana. • Foreign direct investment and trade openness drive renewable energy. • Real GDP per capita plays an inconsequential role in renewable development. • A dynamic forecast reveals an uninspiring future of renewable energy. • Determinant-oriented policies are needed for Ghana's renewable energy development. [ABSTRACT FROM AUTHOR]