Forecasting Hierarchical Time Series in Power Generation

Gontijo, Tiago Silveira; Costa, Marcelo Azevedo

doi:10.3390/en13143722

Cited by 11 publications

(12 citation statements)

References 36 publications

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“…The publication [17] has displayed a novel hybrid model ANFIS which consolidates both ANN and fuzzy frameworks for prediction future power utilization; the result has proved that this hybridizing approach has the potential of improving prediction performance since it has more significant accuracy and leads to smaller errors contrasted with other models. Likewise, the advantages of the hybrid approach were also verified by many studies [18][19][20]。.…”

Section: Literature Reviewmentioning

confidence: 59%

Appling an Improved Method Based on ARIMA Model to Predict the Short‐Term Electricity Consumption Transmitted by the Internet of Things (IoT)

Guo

Chen

Wang

et al. 2021

Wireless Communications and Mobile Computing

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The rapid development of the Internet of Things (IoT) has brought a data explosion and a new set of challenges. It has been an emergency to construct a more robust and precise model to predict the electricity consumption data collected from the Internet of Things (IoT). Accurately forecasting the electricity consumption is a crucial technology for the planning of the energy resource which could lead to remarkable conservation of the building electricity consumption. This paper is focused on the electricity consumption forecasting of an office building with a small-scale dataset, and 117 daily electricity consumption of the building are involved in the dataset, among which 89 values are selected as the training dataset and the remaining 28 values as the testing dataset. The hybrid model ARIMA (autoregression integrated moving average)-SVR (support vector regression) is proposed to predict the electricity consumption with different prediction horizons ranging from 1 day to 28 days. The model performances are assessed by three evaluation indicators, respectively, are the mean squared error (MSE), the root mean square error (RMSE), and the mean absolute percentage error (MAPE). The proposed model ARIMA-SVR is compared with the other four models, respectively, are the ARIMA, ARIMA-GBR (gradient boosting regression), LSTM (long short-term memory), and GRU (gated recurrent unit) models. The experiment result shows that the ARIMA-SVR model has lower prediction errors when the prediction horizon is within 20 days, and the ARIMA model is better when the prediction horizon is in the interval of 20 to 28 days. The provided method ARIMA-SVR has higher flexibility, and it is a great choice for electricity consumption prediction with more accurate results.

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Section: Literature Reviewmentioning

confidence: 59%

Appling an Improved Method Based on ARIMA Model to Predict the Short‐Term Electricity Consumption Transmitted by the Internet of Things (IoT)

Guo

Chen

Wang

et al. 2021

Wireless Communications and Mobile Computing

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“…To represent the bottom-up approach using Eq (1) , where 0 i × j is the i × j null matrix, the role of P is to extract the bottom-level forecasts, which are subsequently aggregated by the summation matrix S to provide the revised forecasts for the whole hierarchy. For more detail, refer to [ 6 , 8 ].…”

Section: Methodsmentioning

confidence: 99%

“…The results show that the regression-based, VAR-COV, and Rank-based methods perform better compared to the other methods. Silveira and Azevedo [ 6 ], analyzed hourly power generation in Brazil (2018–2020), grouped according to each of the electrical subsystems and their respective sources of generating energy. The objective was to calculate the accuracy of the main measures of aggregating and disaggregating the forecasts of the Autoregressive Integrated Moving Average and Error, Trend, and Seasonal models (ETS).…”

Section: Introductionmentioning

confidence: 99%

Enhancing forecast accuracy using combination methods for the hierarchical time series approach

Mohamed

2023

PLoS ONE

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This study aims to investigate whether combining forecasts generated from different models can improve forecast accuracy rather than individual models using the hierarchical time series. Various approaches of hierarchical forecasting have been considered; a bottom-up, top-down, and an optimal combination approach. Autoregressive moving averages (ARIMA) and exponential smoothing (ETS) were used as forecasting models in creating forecasting for all levels in the hierarchy to show the effect of different forecasting methods for each hierarchical model. The results indicated that the Minimum Trace Sample estimator (MinT-Sample) and the bottom-up approaches with the ARIMA model have good predictive performance than other approaches. Moreover, the forecasts from the MinT-Sample and bottom-up approaches were combined using five different combining methods. The experimental results showed that the (AC) method is superior to all other combining methods and more accurate than other individual models at level zero (international total trade in Egypt) and level one (total exports, and total imports). So, combining forecasts generated from different models by hierarchical time series leads to more accurate forecasting of the value of imports and exports which will improve the overall international trade performance, and that is through using the forecasting values of imports and exports to plan for improving the trade balance and drawing up a more efficient production policy. Finally, the study recommends using hierarchical forecasting methods in the areas of international trade, and the Ministry of Commerce and Industry could adopt the results of this study to produce precise forecasts for international trade. Moreover, the results of this study are to be a guide for the researchers to apply these approaches in other fields to improve the performance of forecasting.

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“…Focused on the analysis of the electrical system about electricity generation in Brazil, many works evaluated the generation capacity concerning a hydroelectric source, as presented in Brito et al [32] and Fredo et al [33], or as a hydrothermal problem as discussed by Finardi et al [34] and van Ackooij et al [35]. Moreover, according to Silveira Gontijo and Azevedo Costa [36] in Brazil, there is a predominance of hydroelectric generation (73%), which makes the analysis of hydroelectric energy price forecasting an important field of study.…”

Section: Related Workmentioning

confidence: 99%

Electricity Price Forecasting Based on Self-Adaptive Decomposition and Heterogeneous Ensemble Learning

et al. 2020

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Electricity price forecasting plays a vital role in the financial markets. This paper proposes a self-adaptive, decomposed, heterogeneous, and ensemble learning model for short-term electricity price forecasting one, two, and three-months-ahead in the Brazilian market. Exogenous variables, such as supply, lagged prices and demand are considered as inputs signals of the forecasting model. Firstly, the coyote optimization algorithm is adopted to tune the hyperparameters of complementary ensemble empirical mode decomposition in the pre-processing phase. Next, three machine learning models, including extreme learning machine, gradient boosting machine, and support vector regression models, as well as Gaussian process, are designed with the intent of handling the components obtained through the signal decomposition approach with focus on time series forecasting. The individual forecasting models are directly integrated in order to obtain the final forecasting prices one to three-months-ahead. In this case, a grid of forecasting models is obtained. The best forecasting model is the one that has better generalization out-of-sample. The empirical results show the efficiency of the proposed model. Additionally, it can achieve forecasting errors lower than 4.2% in terms of symmetric mean absolute percentage error. The ranking of importance of the variables, from the smallest to the largest is, lagged prices, demand, and supply. This paper provided useful insights for multi-step-ahead forecasting in the electrical market, once the proposed model can enhance forecasting accuracy and stability.

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Forecasting Hierarchical Time Series in Power Generation

Cited by 11 publications

References 36 publications

Appling an Improved Method Based on ARIMA Model to Predict the Short‐Term Electricity Consumption Transmitted by the Internet of Things (IoT)

Appling an Improved Method Based on ARIMA Model to Predict the Short‐Term Electricity Consumption Transmitted by the Internet of Things (IoT)

Enhancing forecast accuracy using combination methods for the hierarchical time series approach

Electricity Price Forecasting Based on Self-Adaptive Decomposition and Heterogeneous Ensemble Learning

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