摘要 :
Forecasting peak electrical energy consumption is important because it allows utilities to properly plan for the production and distribution of electrical energy. This reduces operating costs and avoids power outages. In addition,...
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Forecasting peak electrical energy consumption is important because it allows utilities to properly plan for the production and distribution of electrical energy. This reduces operating costs and avoids power outages. In addition, it can help reduce environmental impact by allowing for more efficient power generation and reducing the need for additional fossil fuels during periods of high demand. In the current work, electric power consumption data from “Compagnie Electrique du Benin (CEB)” was used to deduce the peak electric power consumption at peak hours. The peak consumption of electric power was predicted using hybrid approaches based on traditional time series prediction methods (autoregressive integrated moving average (ARIMA)) and deep learning methods (long short-term memory (LSTM), gated recurrent unit (GRU)). The ARIMA approach was used to model the trend term, while deep learning approaches were employed to interpret the fluctuation term, and the outputs from these models were combined to provide the final result. The hybrid approach, ARIMA-LSTM, provided the best prediction performance with root mean square error (RMSE) of 7.35, while for the ARIMA-GRU hybrid approach, the RMSE was 9.60. Overall, the hybrid approaches outperformed the single approaches, such as GRU, LSTM, and ARIMA, which exhibited RMSE values of 18.11, 18.74, and 49.90, respectively.
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摘要 :
State‐of‐health (SOH) estimation is one of the most critical battery management system (BMS) tasks. A challenge remains for the SOH prediction due to the complicated battery aging mechanism. The most common health indicator is t...
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State‐of‐health (SOH) estimation is one of the most critical battery management system (BMS) tasks. A challenge remains for the SOH prediction due to the complicated battery aging mechanism. The most common health indicator is the capacity of the lithium‐ion battery. The fluctuation of capacity caused by the capacity regeneration phenomenon can seriously affect the prediction performance. A new complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and gate recurrent unit (GRU) based fusion prediction model for SOH estimation is proposed to solve the problem effectively. First, the CEEMDAN algorithm decomposes the original SOH into local fluctuations and global degradation trends. Then, the GRU network and autoregressive integrated moving average model are used to predict the above trends, respectively. Next, a sliding window is designed to calculate an average value of the global degradation trend prediction residuals. Then, the second GRU algorithm can be used to correct prediction residuals. Finally, the prediction results of the aforementioned parts are combined to obtain the final SOH estimation. The proposed method is verified by experimental battery data from NASA and CALCE datasets. The results show that the fusion method has both higher estimation accuracy and stronger robustness than other methods.
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Sugarcane (Saccharum officinarum L.) is one of the principal origins of sugar and is also known as the main cash
crop of India. About 19.07% of the total production of the world’s sugar requirement is fulfilled by India.
Tradit...
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Sugarcane (Saccharum officinarum L.) is one of the principal origins of sugar and is also known as the main cash
crop of India. About 19.07% of the total production of the world’s sugar requirement is fulfilled by India.
Traditionally, Statistical approaches have been utilized for Crop yield prediction, which is tedious and timeconsuming.
In this direction, the present work proposed a novel hybrid CNN-Bi-LSTM_CYP deep learningbased
approach that includes convolutional layers to extract the relevant spatial information in a sequence to
Bi-LSTM layers that recognize the Phenological long-term and short-term bidirectional dependencies in the
dataset to predict the Sugarcane crop yield. The experimentation was performed and validated on the historical
dataset from 1950 to 2019 years of the major Sugarcane-producing states of India. The preliminary results shown
that the CNN-Bi-LSTM_CYP method performed well (RMSE:4.05, MSE:16.40) in comparison to traditional
Stacked-LSTM (RMSE:8.8, MSE:77.79), ARIMA (RMSE:5.9, MSE:34.80), GPR (RMSE:10.1, MSE:103.3), and Holtwinter
Time-series (RMSE:9.9, MSE:99.7) techniques. The study concluded that the predicted sugar yield has a
minimal relative error concerning the ground truth data for the CNN-Bi-LSTM_CYP approach proving the proposed
model’s efficiency.
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Solar renewable energy (SRE) applications are substantial in eradicating the rising global energy shortages and reversing the approaching environmental apocalypse. Hence, effective solar irradiance forecasting models are crucial i...
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Solar renewable energy (SRE) applications are substantial in eradicating the rising global energy shortages and reversing the approaching environmental apocalypse. Hence, effective solar irradiance forecasting models are crucial in utilizing SRE efficiently. This paper introduces a partially amended hybrid model (PAHM) by the implementation of a new algorithm. The algorithm innovatively utilizes bi-directional gated unit (Bi-GRU), autoregressive integrated moving average (ARIMA) and naive decomposition models to predict solar irradiance in 5-min and 60-min intervals. Meanwhile, the models' generalizability strengths would be tested under an 11-fold cross-validation and are further classified according to their computational costs. The dataset consists of 32 months' solar irradiance and weather conditions records. A fundamental result of this study was that the single models (Bi-GRU and ARIMA) outperformed the hybrid models (PAHM, classical hybrid model) in the 5-min predictions, negating the assumptions that hybrid models oust single models in every time interval. PAHM provided the highest accuracy level in the 60-min predictions and improved the accuracy levels of the classical hybrid model by 5%, on average. The single models were rigorous under the 11-fold cross-validation, performing well with different datasets; although the computational efficiency of the Bi-GRU model was, by far, the best among the models.
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摘要 :
Several machine learning and deep learning models were reported in the literature to forecast COVID-19 but there is no comprehensive report on the comparison between statistical models and deep learning models. The present work re...
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Several machine learning and deep learning models were reported in the literature to forecast COVID-19 but there is no comprehensive report on the comparison between statistical models and deep learning models. The present work reports a comparative time-series analysis of deep learning techniques (Recurrent Neural Networks with GRU and LSTM cells) and statistical techniques (ARIMA and SARIMA) to forecast the country-wise cumulative confirmed, recovered, and deaths. The Gated Recurrent Units (GRU), Long Short-Term Memory (LSTM) cells based on Recurrent Neural Networks (RNN), ARIMA and SARIMA models were trained, tested, and optimized to forecast the trends of the COVID-19. We deployed python to optimize the parameters of ARIMA which include (p, d, q) representing autoregressive and moving average terms and parameters of SARIMA model include additional seasonal terms which are denoted by (P, D, Q). Similarly, for LSTM and GRU based RNN models’ parameters (number of layers, hidden size, learning rate and number of epochs) are optimized by deploying PyTorch machine learning framework. The best model was chosen based on the lowest Mean Square Error (MSE) and Root Mean Squared Error (RMSE) values. For most of the time-series data of the countries, deep learning-based models LSTM and GRU outperformed statistical ARIMA and SARIMA models, with an RMSE values that are 40 folds less than that of the ARIMA models. But for some countries statistical (ARIMA, SARIMA) models outperformed deep learning models. Further, we emphasize the importance of various factors such as age, preventive measures and healthcare facilities etc. that play vital role on the rapid spread of COVID-19 pandemic.
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