Effective electricity energy consumption forecasting using echo state network improved by differential evolution algorithm

Wang, Lin; Hu, Huanling; Ai, Xue-Yi; Liu, Hua

doi:10.1016/j.energy.2018.04.078

Cited by 108 publications

(38 citation statements)

References 48 publications

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“…In Figure 6, the mean wind speed per month management, and wind forecasting. It would be interesting to study the potential of AI-based forecasting [29,30] in this context. management, and wind forecasting.…”

Section: Resultsmentioning

confidence: 99%

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An Assessment of Wind Energy Potential for the Three Topographic Regions of Eritrea

2020

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This paper presents the wind energy potential and wind characteristics for 25 wind sites in Eritrea, based on wind data from the years 2000–2005. The studied sites are distributed all over Eritrea, but can roughly be divided into three regions: coastal region, western lowlands, and central highlands. The coastal region sites have the highest potential for wind power. An uncertainty, due to extrapolating the wind speed from the 10-m measurements, should be noted. The year to year variations are typically small and, for the sites deemed as suitable for wind power, the seasonal variations are most prominent in the coastal region with a peak during the period November–March. Moreover, Weibull parameters, prevailing wind direction, and wind power density recalculated for 100 m above ground are presented for all 25 sites. Comparing the results to values from the web-based, large-scale dataset, the Global Wind Atlas (GWA), both mean wind speed and wind power density are typically higher for the measurements. The difference is especially large for the more complex-terrain central highland sites where GWA results are also likely to be more uncertain. The result of this study can be used to make preliminary assessments on possible power production potential at the given sites.

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Section: Resultsmentioning

confidence: 99%

“…management, and wind forecasting. It would be interesting to study the potential of AI-based forecasting [29,30] in this context. Comparing the three regions to each other, sites in the coastal region have typically the highest wind power density followed by the central highlands.…”

Section: Resultsmentioning

confidence: 99%

An Assessment of Wind Energy Potential for the Three Topographic Regions of Eritrea

2020

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“…Although deep learning algorithms such as LSTM and ESN have gain great popularity, as both a long‐ and short‐term memory network, LSTM is a kind of temporal recursive neural network, suitable for processing and predicting important events with relatively long interval and delay in time series . ESN requires a scale much larger than the node size of the general neural network, and the most mature application of ESN is still focused on the learning of time series . Since supervised learning is unable be carried out, a longer training process is required due to the optimization of the training output weight by the genetic algorithm.…”

Section: Introductionmentioning

confidence: 99%

The Prediction of liquid holdup in horizontal pipe with BP neural network

Rong-ge

Sun

et al. 2020

Energy Science & Engineering

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Liquid holdup is one of the most critical factors for the formation of pipe effusion, which is closely related to the efficiency of pipe transportation. Nowadays, liquid holdup is mainly estimated according to empirical or semiempirical correlation. Besides, little has been done concerning the accurate prediction of liquid holdup. Therefore, to obtain more precise forecast, this paper proposed a prediction method concerning liquid holdup in horizontal pipe with BP neural network algorithm. Meanwhile, a sensitivity analysis on the key factors impacting liquid holdup was conducted by the combination of the forecast calculation and orthogonal experiment design. The results showed that compared with the empirical calculation (the smallest standard deviation 8.65%), the BP neural network prediction model had achieved more accurate estimation (the average relative error is 7.38%). In addition, the sensitivity analysis indicated that the main indexes including pipe diameter, gas‐ and liquid‐phase superficial velocities, and temperature have significant influence on the liquid holdup. Pipe diameter, liquid‐phase superficial velocity, temperature, and viscosity are positively correlated with the liquid holdup, while pressure and gas‐phase superficial velocity are negatively correlated with it.

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“…Then, the researches on tackling by optimization techniques in many applications have become a fruitful field of research, especially those interested in solving global optimization problems. The swarm intelligence optimization (SIO) algorithm is a kind of bionic random method inspired by natural phenomena and biological behaviors and can deal with certain high-dimensional complex and variable optimization problems because of its better computing performance and simple model [3,4].…”

Section: Introductionmentioning

confidence: 99%

Improved Monarch Butterfly Optimization Algorithm Based on Opposition‐Based Learning and Random Local Perturbation

Sun

Chen

et al. 2019

Complexity

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Many optimization problems have become increasingly complex, which promotes researches on the improvement of different optimization algorithms. The monarch butterfly optimization (MBO) algorithm has proven to be an effective tool to solve various kinds of optimization problems. However, in the basic MBO algorithm, the search strategy easily falls into local optima, causing premature convergence and poor performance on many complex optimization problems. To solve the issues, this paper develops a novel MBO algorithm based on opposition-based learning (OBL) and random local perturbation (RLP). Firstly, the OBL method is introduced to generate the opposition-based population coming from the original population. By comparing the opposition-based population with the original population, the better individuals are selected and pass to the next generation, and then this process can efficiently prevent the MBO from falling into a local optimum. Secondly, a new RLP is defined and introduced to improve the migration operator. This operation shares the information of excellent individuals and is helpful for guiding some poor individuals toward the optimal solution. A greedy strategy is employed to replace the elitist strategy to eliminate setting the elitist parameter in the basic MBO, and it can reduce a sorting operation and enhance the computational efficiency. Finally, an OBL and RLP-based improved MBO (OPMBO) algorithm with its complexity analysis is developed, following on which many experiments on a series of different dimensional benchmark functions are performed and the OPMBO is applied to clustering optimization on several public data sets. Experimental results demonstrate that the proposed algorithm can achieve the great optimization performance compared with a few state-of-the-art algorithms in most of the test cases.

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Effective electricity energy consumption forecasting using echo state network improved by differential evolution algorithm

Cited by 108 publications

References 48 publications

An Assessment of Wind Energy Potential for the Three Topographic Regions of Eritrea

An Assessment of Wind Energy Potential for the Three Topographic Regions of Eritrea

The Prediction of liquid holdup in horizontal pipe with BP neural network

Improved Monarch Butterfly Optimization Algorithm Based on Opposition‐Based Learning and Random Local Perturbation

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