Sensitivity analysis for forecasting Brazilian electricity demand using artificial neural networks and hybrid models based on Autoregressive Integrated Moving Average

Luzia, Ruan; Rubio, Lihki; Velasquez, Carlos E.

doi:10.1016/j.energy.2023.127365

Cited by 28 publications

(5 citation statements)

References 26 publications

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“…The use of neural networks, particularly Long Short-Term Memory (LSTM) networks and transformer models, has emerged as an effective strategy in electrical consumption estimation. In [25], the effectiveness of LSTMs in capturing complex temporal patterns in electrical data is highlighted. Additionally, transformer models have shown great potential in sequence processing, as discussed in [26], making them valuable tools for accurate electrical consumption prediction and energy management optimization.…”

Section: Related Workmentioning

confidence: 99%

Comparative Analysis of Deep Learning Methods for Fault Avoidance and Predicting Demand in Electrical Distribution

Schröder,

Farias,

Dormido-Canto

et al. 2024

Energies

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In recent years, the distribution network in Chile has undergone various modifications to meet new demands and integrate new technologies. However, these improvements often do not last as long as expected due to inaccurate forecasting, resulting in frequent equipment changes and service interruptions. These issues affect project investment, unsold energy, and penalties for poor quality of supply. Understanding the electricity market, especially in distribution, is crucial and requires linking technical quality standards with service quality factors, such as the frequency and duration of interruptions, to understand their impact on regulated distribution to customers. In this context, a comparative study will be carried out between Long Short-Term Memory (LSTM) and transformer architectures, with the aim of improving the sizing of distribution transformers and preventing failures when determining the nominal power of the transformer to be installed. Variables such as voltages and operating currents of transformers installed between 2020 and 2021 in the Valparaíso region, Chile, along with the type and number of connected customers, maximum and minimum temperatures of the sectors of interest, and seasonality considerations will be used. The compilation of previous studies and the identification of key variables will help to propose solutions based on error percentages to optimise the accuracy of transformer sizing.

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Section: Related Workmentioning

confidence: 99%

Comparative Analysis of Deep Learning Methods for Fault Avoidance and Predicting Demand in Electrical Distribution

Schröder,

Farias,

Dormido-Canto

et al. 2024

Energies

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“… Autoregressive Integrated Moving Average (ARIMA) combines AR and MA methods and can address time series data that is non-stationary (data that has statistical properties changing over time) by differencing. It might be applied to power load data that has both trend and seasonality 21 . Exponential Smoothing consider both the trend and seasonality.…”

Section: Related Workmentioning

confidence: 99%

Deep learning-driven hybrid model for short-term load forecasting and smart grid information management

Wen,

Liao,

Niu

et al. 2024

Sci Rep

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Accurate power load forecasting is crucial for the sustainable operation of smart grids. However, the complexity and uncertainty of load, along with the large-scale and high-dimensional energy information, present challenges in handling intricate dynamic features and long-term dependencies. This paper proposes a computational approach to address these challenges in short-term power load forecasting and energy information management, with the goal of accurately predicting future load demand. The study introduces a hybrid method that combines multiple deep learning models, the Gated Recurrent Unit (GRU) is employed to capture long-term dependencies in time series data, while the Temporal Convolutional Network (TCN) efficiently learns patterns and features in load data. Additionally, the attention mechanism is incorporated to automatically focus on the input components most relevant to the load prediction task, further enhancing model performance. According to the experimental evaluation conducted on four public datasets, including GEFCom2014, the proposed algorithm outperforms the baseline models on various metrics such as prediction accuracy, efficiency, and stability. Notably, on the GEFCom2014 dataset, FLOP is reduced by over 48.8%, inference time is shortened by more than 46.7%, and MAPE is improved by 39%. The proposed method significantly enhances the reliability, stability, and cost-effectiveness of smart grids, which facilitates risk assessment optimization and operational planning under the context of information management for smart grid systems.

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“…de Oliveria and Oliveria (2018) forecasts medium-term electricity load using an autoregressive integrated moving average model (ARIMA) with a seasonal trend decomposition model combining weighted regression. Luzia et al (2023) forecasts Brazilian electricity demand with ARIMA combined with Wavelet Transform and Fourier Transform. Wang (2022) utilities ARIMA combined with BP neural network to predict per capita coal consumption of China.…”

Section: Introductionmentioning

confidence: 99%

Electric vehicle charging load prediction based on variational mode decomposition and Prophet-LSTM

Cheng,

Zheng,

Ruan

et al. 2023

Front. Energy Res.

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With the large-scale development of electric vehicles, the accuracy of electric vehicle charging load prediction is increasingly important for electric power system. Accurate EV charging load prediction is essential for the efficiency of electric system planning and economic operation of electric system. This paper proposes an electric vehicle charging load predicting method based on variational mode decomposition and Prophet-LSTM. Firstly, the variational mode decomposition algorithm is used to decompose the charging load into several intrinsic mode functions in order to explore the characteristics of EV charging load data. Secondly, in order to make full use of the advantages of various forecasting methods, the intrinsic mode functions are classified into low and high frequency sequences based on their over-zero rates. The high and low frequency sequences are reconstructed to obtain two frequency sequences. Then the LSTM neural network and Prophet model are used to predict the high and low frequency sequences, respectively. Finally, the prediction results obtained from the prediction of high frequency and low frequency sequences are combined to obtain the final prediction result. The assessment of the prediction results shows that the prediction accuracy of the prediction method proposed in this paper is improved compared to the traditional prediction methods, and the average absolute error is lower than that of ARIMA, LSTM and Prophet respectively by 7.57%, 8.73%, and 46.02%. The results show that the prediction method proposed in this paper has higher prediction accuracy than the traditional methods, and is effective in predicting EV charging load.

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Sensitivity analysis for forecasting Brazilian electricity demand using artificial neural networks and hybrid models based on Autoregressive Integrated Moving Average

Cited by 28 publications

References 26 publications

Comparative Analysis of Deep Learning Methods for Fault Avoidance and Predicting Demand in Electrical Distribution

Comparative Analysis of Deep Learning Methods for Fault Avoidance and Predicting Demand in Electrical Distribution

Deep learning-driven hybrid model for short-term load forecasting and smart grid information management

Electric vehicle charging load prediction based on variational mode decomposition and Prophet-LSTM

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