Holt–Winters smoothing enhanced by fruit fly optimization algorithm to forecast monthly electricity consumption

Jiang, Weiheng; Wu, Xiaogang; Gong, Yi; Yu, Wanxin; Zhong, Xiaohui

doi:10.1016/j.energy.2019.116779

Cited by 122 publications

(43 citation statements)

References 36 publications

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“…Within classical methods, the accuracy of the forecasts in the Holt-Winters models has been widely studied, especially how to select the best method and adjust the parameters [30]. As the Holt-Winters models are recursive, an initialization value is needed to feed the model.…”

Section: Related Workmentioning

confidence: 99%

Stability of Multiple Seasonal Holt-Winters Models Applied to Hourly Electricity Demand in Spain

2020

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Electricity management and production depend heavily on demand forecasts made. Any mismatch between the energy demanded with respect to that produced supposes enormous losses for the consumer. Transmission System Operators use time series-based tools to forecast accurately the future demand and set the production program. One of the most effective and highly used methods are Holt-Winters. Recently, the incorporation of the multiple seasonal Holt-Winters methods has improved the accuracy of the predictions. These forecasts, depend greatly on the parameters with which the model is constructed. The forecasters need to deal with these parameters values when operating the model. In this article, the parameters space of the multiple seasonal Holt-Winters models applied to electricity demand in Spain is analysed and discussed. The parameters stability analysis leads to forecasters better understanding the behaviour of the predictions and managing their exploitation efficiently. The analysis addresses different time windows, depending on the period of the year as well as different training set sizes. The results show the influence of the calendar effect on these parameters and if it is necessary or not to update them in order to obtain a good accuracy over time.

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Section: Related Workmentioning

confidence: 99%

Stability of Multiple Seasonal Holt-Winters Models Applied to Hourly Electricity Demand in Spain

2020

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“…e Holt-Winter's method is one of several exponential smoothing methods that have the power to directly analyse time series data. Several academic papers used the Holt-Winters method in the past to predict electricity demands [6,11,12]. Holt-Winters method falls under the category of exponential smoothening problems which are affected by some few factors.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Holt-Winters method falls under the category of exponential smoothening problems which are affected by some few factors. e first factor known as lag implies that the prediction often lags behind the actual trends [12,13]. Also, exponential smoothening may be best suited for forecasts that are short-term rather than seasonal or cyclic.…”

Section: Literature Reviewmentioning

confidence: 99%

Time Series Prediction of Electricity Demand Using Adaptive Neuro-Fuzzy Inference Systems

Acakpovi

Ternor

Asabere

et al. 2020

Mathematical Problems in Engineering

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This paper is concerned with the reliable prediction of electricity demands using the Adaptive Neuro-Fuzzy Inference System (ANFIS). The need for electricity demand prediction is fundamental and vital for power resource planning and monitoring. A dataset of electricity demands covering the period of 2003 to 2018 was collected from the Electricity Distribution Company of Ghana, covering three urban areas namely Mallam, Achimota, and Ga East, all in Ghana. The dataset was divided into two parts: one part covering a period of 0 to 500 hours was used for training of the ANFIS algorithm while the second part was used for validation. Three scenarios were considered for the simulation exercise that was done with the MATLAB software. Scenario one considered four inputs sampled data, scenario two considered an additional input making it 5, and scenario 3 was similar to scenario 1 with the exception of the number of membership functions that increased from 2 to 3. The performance of the ANFIS algorithm was assessed by comparing its predictions with other three forecast models namely Support Vector Regression (SVR), Least Square Support Vector Machine (LS-SVM), and Auto-Regressive Integrated Moving Average (ARIMA). Findings revealed that the ANFIS algorithm can perform the prediction accurately, the ANFIS algorithm converges faster with an increase in the data used for training, and increasing the membership function resulted in overfitting of data which adversely affected the RMSE values. Comparison of the ANFIS results to other previously used methods of predicting electricity demands including SVR, LS-SVM, and ARIMA revealed that there is merit to the potentials of the ANFIS algorithm for improved predictive accuracy while relying on a quality data for training and reliable setting of tuning parameters.

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“…Em [Jiang et al 2020] são utilizados modelos de Holt-Winters para prever o consumo por eletricidade em uma cidade da China, os autores apresentam uma metodologia para selecionar os melhores parâmetros na aplicação do método, chegando a um resultado preciso, mesmo para amostras pequenas.…”

Section: Trabalhos Relacionadosunclassified

Previsão da demanda de energia elétrica a curto prazo para Região Sul do Brasil utilizando o Modelo ARIMA e o Modelo de Suavização Exponencial de Holt (SEH)

Amaral¹,

Silveira²,

Mattos³

et al. 2020

Anais Do Encontro Nacional De Inteligência Artificial E Computacional (ENIAC 2020)

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Prever a demanda por energia elétrica é tarefa essencial para estratégia econômica da nação, sendo de extrema necessidade ocorrer um balanceamento adequado entre geração de distribuição. Neste contexto, este trabalho realizou a previsão da demanda de energia elétrica na Região Sul do Brasil utilizando modelagem da série temporal de dados de consumo residencial. Foram utilizados o Modelo de Suavização Exponencial de Holt e o Modelo ARIMA. Os resultados alcançados mostraram que os modelos foram capazes de se ajustar adequadamente aos dados em curto prazo, apresentando baixo erro de validação que pode ser explicado pela crise econômica global de 2008.

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Holt–Winters smoothing enhanced by fruit fly optimization algorithm to forecast monthly electricity consumption

Cited by 122 publications

References 36 publications

Stability of Multiple Seasonal Holt-Winters Models Applied to Hourly Electricity Demand in Spain

Stability of Multiple Seasonal Holt-Winters Models Applied to Hourly Electricity Demand in Spain

Time Series Prediction of Electricity Demand Using Adaptive Neuro-Fuzzy Inference Systems

Previsão da demanda de energia elétrica a curto prazo para Região Sul do Brasil utilizando o Modelo ARIMA e o Modelo de Suavização Exponencial de Holt (SEH)

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