Optimization of neural network with wavelet transform and improved data selection using bat algorithm for short-term load forecasting

Bento, Pedro; Pombo, J.A.N.; Calado, M.R.A.; Mariano, S.J.P.S.

doi:10.1016/j.neucom.2019.05.030

Cited by 71 publications

(28 citation statements)

References 36 publications

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“…Before training and use, the network data are usually preprocessed for transformation into a new series that could be more efficiently processed to achieve more accurate predictions. A number of works [33,[38][39][40][41] have pointed out that splitting the time series into several sub-series, each of which retains a particular behavior of the original series, can improve forecasting accuracy. One of the simplest such decompositions is to split the time series into its trend and fluctuations.…”

Section: Resultsmentioning

confidence: 99%

Applying Artificial Neural Networks to Forecast European Union Allowance Prices: The Effect of Information from Pollutant-Related Sectors

Jaramillo-Morán

García-García

2019

Energies

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In this paper, we forecast the price of CO2 emission allowances using an artificial intelligence tool: neural networks. We were able to provide confident predictions of several future prices by processing a set of past data. Different model structures were tested. The influence of subjective economic and political decisions on price evolution leads to complex behavior that is hard to forecast. We analyzed correlations with different economic variables related to the price of CO2 emission allowances and found the behavior of two to be similar: electricity prices and iron and steel prices. They, along with CO2 emission allowance prices, were included in the forecasting model in order to verify whether or not this improved forecasting accuracy. Only slight improvements were observed, which proved to be more significant when their respective time series trends or fluctuations were used instead of the original time series. These results show that there is some sort of link between the three variables, suggesting that the price of CO2 emission allowances is closely related to the time evolution of the price of electricity and that of iron and steel, which are very pollutant industrial sectors. This can be regarded as evidence that the CO2 market is working properly.

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Section: Resultsmentioning

confidence: 99%

Applying Artificial Neural Networks to Forecast European Union Allowance Prices: The Effect of Information from Pollutant-Related Sectors

Jaramillo-Morán

García-García

2019

Energies

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“…Khaldi et al [31] studied the effect of multi-step prediction strategies on the performance of long-and short-term recursive neural network models (SRN, LSTM, and GRU), and finally proposed corresponding strategies for the three models. Bento et al [32] proposed a novel Bat-inspired hybrid method integrating bat algorithm and scaled conjugate gradient algorithm to improve the learning ability of neural networks. Khaldi et al [16] studied the artificial neural network (ANN) combined with a signal decomposition technique to predict the weekly emergency department arrivals in hospitals.…”

Section: Literaturementioning

confidence: 99%

Medical service demand forecasting using a hybrid model based on ARIMA and self-adaptive filtering method

Huang

et al. 2020

BMC Med Inform Decis Mak

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Background Accurate forecasting of medical service demand is beneficial for the reasonable healthcare resource planning and allocation. The daily outpatient volume is characterized by randomness, periodicity and trend, and the time series methods, like ARIMA are often used for short-term outpatient visits forecasting. Therefore, to further enlarge the prediction horizon and improve the prediction accuracy, a hybrid prediction model integrating ARIMA and self-adaptive filtering method is proposed. Methods The ARIMA model is first used to identify the features like cyclicity and trend of the time series data and to estimate the model parameters. The parameters are then adjusted by the steepest descent algorithm in the adaptive filtering method to reduce the prediction error. The hybrid model is validated and compared with traditional ARIMA by several test sets from the Time Series Data Library (TSDL), a weekly emergency department (ED) visit case from literature study, and the real cases of prenatal examinations and B-ultrasounds in a maternal and child health care center (MCHCC) in Ningbo. Results For TSDL cases the prediction accuracy of the hybrid prediction is improved by 80–99% compared with the ARIMA model. For the weekly ED visit case, the forecasting results of the hybrid model are better than those of both traditional ARIMA and ANN model, and similar to the ANN combined data decomposition model mentioned in the literature. For the actual data of MCHCC in Ningbo, the MAPE predicted by the ARIMA model in the two departments was 18.53 and 27.69%, respectively, and the hybrid models were 2.79 and 1.25%, respectively. Conclusions The hybrid prediction model outperforms the traditional ARIMA model in both accurate predicting result with smaller average relative error and the applicability for short-term and medium-term prediction.

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“…A previsão de demanda de carga é de grande relevância para os agentes da operação de sistemas elétricos de potência. A operação é fortemente dependente desta informação para o correto funcionamento de geradores, subestações e linhas [1], [2]. Através desta tarefa, os agentes do setor elétrico podem estimar a compra de energia baseada em demandas futuras e preços, minimizando a diferença entre a quantidade comprada e consumida [3].…”

Section: Introductionunclassified

“…Em contrapartida, com o desenvolvimento nas últimas décadas da inteligência computacional, modelos não-lineares como as redes neurais artificiais (RNAs) também têm sido considerados para previsão de cargas [15]. Tais métodos têm apresentando resultados satisfatórios em diversos contextos [2], [8], [16]. Em [8] os autores propõem a utilização de uma rede recorrente de Elman otimizada pelo algoritmo Otimização por Enxame de Partículas (Particle Swarm Optimization -PSO) para a tarefa.…”

Section: Introductionunclassified

Previsão de Carga Baseada em Ensemble de Modelos Inteligentes

Júnior¹,

Oliveira²,

Siqueira³

et al. 2020

Anais Do 14. Congresso Brasileiro De Inteligência Computacional

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Optimization of neural network with wavelet transform and improved data selection using bat algorithm for short-term load forecasting

Cited by 71 publications

References 36 publications

Applying Artificial Neural Networks to Forecast European Union Allowance Prices: The Effect of Information from Pollutant-Related Sectors

Applying Artificial Neural Networks to Forecast European Union Allowance Prices: The Effect of Information from Pollutant-Related Sectors

Medical service demand forecasting using a hybrid model based on ARIMA and self-adaptive filtering method

Previsão de Carga Baseada em Ensemble de Modelos Inteligentes

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