A short-term hybrid forecasting model for time series electrical-load data using random forest and bidirectional long short-term memory

Ferdoush, Zannatul; Mahmud, Booshra Nazifa; Chakrabarty, Amitabha; Uddin, Jia

doi:10.11591/ijece.v11i1.pp763-771

Cited by 14 publications

(9 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many scholars have attempted to predict the electric load in the past, typically using the traditional ANN, time series and other techniques. Researchers used LSTM, Bi-LSTAM and RF-bi-LSTM to forecast the electric load and found that RF-bi-LSTM performed much better based on this statistical mean square error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and root mean square error (RMSE) [20]. The MAPE examination reveals an error of 127 W, which is around 4.1% to 3.2% of the reported range of peak load encountered in a day and is well within the range of meaningful accuracy.…”

Section: Resultsmentioning

confidence: 99%

Comparison of fuzzy time series, ANN and wavelet techniques for short term load forecasting

Khatoon

Ibraheem

Gupta

et al. 2023

IJPEDS

View full text Add to dashboard Cite

The present article presents the load forecasting for a power system (substation) load demands using techniques based on fuzzy time series (FTS), artificial neural network (ANN), and wavelet transform (WT). The mean absolute percentage error (MAPE), integral absolute error (IAE), integral of time multiplied error (ITAE), integral square error (ISE) along with integral time multiplied square error (ITSE) criteria are used for determining the performance indices and minimizing the error. From the investigations of the results obtained in the study, it is inferred that forecasting of electric load based on WT and ANN offers less error as compared to FTS. The suggested integrated model captures the useful properties of artificial neural networks and wavelet transforms in time series and is found to be accurate for real-time data.

show abstract

Section: Resultsmentioning

confidence: 99%

Comparison of fuzzy time series, ANN and wavelet techniques for short term load forecasting

Khatoon

Ibraheem

Gupta

et al. 2023

IJPEDS

View full text Add to dashboard Cite

show abstract

“…In this model, we used the long-term short-term memory (LSTM) method to estimate the economic impact of the COVID-19 outbreak on different regions of the United States [31,37]. When dealing with time series, we used LSTM modelling, an in-depth learning method that is useful when trying to model time series.…”

Section: -4-1 Long Term Short-term Memorymentioning

confidence: 99%

An Analysis of Covid-19 Pandemic Outbreak on Economy using Neural Network and Random Forest

Hasan,

Ahmed,

Ashik

et al. 2023

jist

View full text Add to dashboard Cite

The pandemic disease outbreaks are causing a significant financial crisis affecting the worldwide economy. Machine learning techniques are urgently required to detect, predict and analyze the economy for early economic planning and growth. Consequently, in this paper, we use machine learning classifiers and regressors to construct an early warning model to tackle economic recession due to the cause of covid-19 pandemic outbreak. A publicly available database created by the National Bureau of Economic Research (NBER) is used to validate the model, which contains information about national revenue, employment rate, and workers' earnings of the USA over 239 days (1 January 2020 to 12 May 2020). Different techniques such as missing value imputation, k-fold cross validation have been used to pre-process the dataset. Machine learning classifiers-Multi-layer Perceptron-Neural Network (MLP-NN) and Random Forest (RF) have been used to predict recession. Additionally, machine learning regressors-Long Short-Term Memory (LSTM) and Random Forest (RF) have been used to detect how much recession a country is facing as a result of positive test cases of covid-19 pandemic. Experimental results demonstrate that the MLP-NN and RF classifiers have exhibited average 88.33% and 85% of recession (where 95%, 81%, 89% and 85%, 81%, 89% for revenue, employment rate and workers earnings, respectively) and average 90.67% and 93.67% of prediction accuracy for LSTM and RF regressors (where 92%, 90%, 90%, and 95%, 93%, 93% respectively).

show abstract

“…RF is a supervised learning method that is a decision tree-based algorithm. As the name proposes as forest the RF classifier is an ensemble of decision trees wherever a random vector sample produce each classifier from the input vector [28] and every tree cast a unit vote for the most popular class to classify an input vector, nearly all of the time trained with a bagging method.…”

Section: Random Forestmentioning

confidence: 99%

Fish survival prediction in an aquatic environment using random forest model

Islam

Kashem

Uddin

2021

IJ-AI

Self Cite

View full text Add to dashboard Cite

In the real world, it is very difficult for fish farmers to select the perfect fish species for aquaculture in a specific aquatic environment. The main goal of this research is to build a machine learning that can predict the perfect fish species in an aquatic environment. In this paper, we have utilized a model using random forest (RF). To validate the model, we have used a dataset of aquatic environment for 11 different fishes. To predict the fish species, we utilized the different characteristics of aquatic environment including pH, temperature, and turbidity. As a performance metrics, we measured accuracy, true positive (TP) rate, and kappa statistics. Experimental results demonstrate that the proposed RF-based prediction model shows accuracy 88.48%, kappa statistic 87.11% and TP rate 88.5% for the tested dataset. In addition, we compare the proposed model with the state-of-art models J48, RF, k-nearest neighbor (k-NN), and classification and regression trees (CART). The proposed model outperforms than the existing models by exhibiting the higher accuracy score, TP rate and kappa statistics.

show abstract

A short-term hybrid forecasting model for time series electrical-load data using random forest and bidirectional long short-term memory

Cited by 14 publications

References 24 publications

Comparison of fuzzy time series, ANN and wavelet techniques for short term load forecasting

Comparison of fuzzy time series, ANN and wavelet techniques for short term load forecasting

An Analysis of Covid-19 Pandemic Outbreak on Economy using Neural Network and Random Forest

Fish survival prediction in an aquatic environment using random forest model

Contact Info

Product

Resources

About