scholarly journals Towards Efficient Electricity Forecasting in Residential and Commercial Buildings: A Novel Hybrid CNN with a LSTM-AE based Framework

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1399 ◽  
Author(s):  
Zulfiqar Khan ◽  
Tanveer Hussain ◽  
Amin Ullah ◽  
Seungmin Rho ◽  
Miyoung Lee ◽  
...  
Keyword(s):  
Mean Square Error ◽  
Energy Management ◽  
Hybrid Model ◽  
Residential Buildings ◽  
Research Work ◽  
Commercial Buildings ◽  
Dense Layer ◽  
Mean Square ◽  
Energy Forecasting ◽  
Energy Prediction

Due to industrialization and the rising demand for energy, global energy consumption has been rapidly increasing. Recent studies show that the biggest portion of energy is consumed in residential buildings, i.e., in European Union countries up to 40% of the total energy is consumed by households. Most residential buildings and industrial zones are equipped with smart sensors such as metering electric sensors, that are inadequately utilized for better energy management. In this paper, we develop a hybrid convolutional neural network (CNN) with an long short-term memory autoencoder (LSTM-AE) model for future energy prediction in residential and commercial buildings. The central focus of this research work is to utilize the smart meters’ data for energy forecasting in order to enable appropriate energy management in buildings. We performed extensive research using several deep learning-based forecasting models and proposed an optimal hybrid CNN with the LSTM-AE model. To the best of our knowledge, we are the first to incorporate the aforementioned models under the umbrella of a unified framework with some utility preprocessing. Initially, the CNN model extracts features from the input data, which are then fed to the LSTM-encoder to generate encoded sequences. The encoded sequences are decoded by another following LSTM-decoder to advance it to the final dense layer for energy prediction. The experimental results using different evaluation metrics show that the proposed hybrid model works well. Also, it records the smallest value for mean square error (MSE), mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE) when compared to other state-of-the-art forecasting methods over the UCI residential building dataset. Furthermore, we conducted experiments on Korean commercial building data and the results indicate that our proposed hybrid model is a worthy contribution to energy forecasting.

scholarly journals Electrical Energy Prediction in Residential Buildings for Short-Term Horizons Using Hybrid Deep Learning Strategy

2020 ◽  
Vol 10 (23) ◽  
pp. 8634
Author(s):  
Zulfiqar Ahmad Khan ◽  
Amin Ullah ◽  
Waseem Ullah ◽  
Seungmin Rho ◽  
Miyoung Lee ◽  
...  
Keyword(s):  
Mean Square Error ◽  
Learning Strategy ◽  
Residential Buildings ◽  
Electricity Consumption ◽  
Absolute Error ◽  
Management Policy ◽  
Mean Square ◽  
Load Prediction ◽  
Energy Prediction ◽  
Proposed Model

Smart grid technology based on renewable energy and energy storage systems are attracting considerable attention towards energy crises. Accurate and reliable model for electricity prediction is considered a key factor for a suitable energy management policy. Currently, electricity consumption is rapidly increasing due to the rise in human population and technology development. Therefore, in this study, we established a two-step methodology for residential building load prediction, which comprises two stages: in the first stage, the raw data of electricity consumption are refined for effective training; and the second step includes a hybrid model with the integration of convolutional neural network (CNN) and multilayer bidirectional gated recurrent unit (MB-GRU). The CNN layers are incorporated into the model as a feature extractor, while MB-GRU learns the sequences between electricity consumption data. The proposed model is evaluated using the root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE) metrics. Finally, our model is assessed over benchmark datasets that exhibited an extensive drop in the error rate in comparison to other techniques. The results indicated that the proposed model reduced errors over the individual household electricity consumption prediction (IHEPC) dataset (i.e., RMSE (5%), MSE (4%), and MAE (4%)), and for the appliances load prediction (AEP) dataset (i.e., RMSE (2%), and MAE (1%)).

scholarly journals An Estimation of a Hybrid Log-Poisson Regression Using a Quadratic Optimization Program for Optimal Loss Reserving in Insurance

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Apollinaire Woundjiagué ◽  
Martin Le Doux Mbele Bidima ◽  
Ronald Waweru Mwangi
Keyword(s):  
Regression Model ◽  
Mean Square Error ◽  
Hybrid Model ◽  
Poisson Regression ◽  
Estimation Method ◽  
Error Prediction ◽  
Mean Square ◽  
Poisson Regression Model ◽  
Loss Reserving ◽  
The Mean

In this article, we are interested in developing an alternative estimation method of the parameters of the hybrid log-Poisson regression model. In our previous paper, we have proposed a hybrid log-Poisson regression model where we have derived the analytical expression of the fuzzy parameters. We found that the hybrid model provide better results than the classical log-Poisson regression model according to the mean square error prediction and the goodness of fit index. However, nowhere we have taken into account the optimal value of h(α-cut) which is of greatest importance in fuzzy regressions literature. In this paper, we provide an alternative estimation method of our hybrid model using a quadratic optimization program and the optimized h-value (α-cut). The expected value of fuzzy number is used as a defuzzification procedure to move from fuzzy values to crisp values. We perform the hybrid model with the alternative estimation we are suggesting on two different numerical data to predict incremental payments in loss reserving. From the mean square error prediction, we prove that the alternative estimation of the new hybrid model with an optimized h-value predicts incremental payments better than the classical log-Poisson regression model as well as the same hybrid model with analytical estimation of parameters. Hence we have optimized the outstanding loss reserves.

scholarly journals Probable Forecasting of Epidemic COVID-19 in Using COCUDE Model for the State of Tamilnadu, India

2020 ◽  
Author(s):  
Prasannavenkatesan Theerthagiri
Keyword(s):  
Mean Square Error ◽  
Akaike Information Criterion ◽  
Virus Disease ◽  
Research Work ◽  
Information Criterion ◽  
Mean Square ◽  
Error Metrics ◽  
Machine Learning Model ◽  
Proposed Model ◽  
Performance Error

Abstract The world has been struck due to the dangerous human threat called Corona Virus Disease 2019. This research work proposes a methodology to encounter the future infection rate, curing rate, and decease rate. This uses the artificial intelligence algorithm to design and develop the proposed confirmed, cured, deceased (COCUDE) model. A machine learning model has been developed with several iterations to design the proposed COCUDE model. The Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and Correlated Akaike Information criterion (AICc) metrics are analyzed to check the stationary and quality for the proposed COCUDE model. The prediction results are evaluated by the performance error metrics such as mean square error (MSE) and root mean square error (RMSE), in which the errors are lower for the proposed model. Thus, the prediction results indicate the proposed COCUDE model might accurately predict future COVID-19 infection rates. It might support the corresponding authorities to take the precautious action on the required necessities for the medical and clinical infrastructures and equipment.

Hybrid Modeling of Flat Rolling Process during Hot Rolling

2013 ◽  
Vol 762 ◽  
pp. 277-282 ◽  
Author(s):  
Olive Chakraborty ◽  
Sushant Rath
Keyword(s):  
Pattern Recognition ◽  
Root Mean Square Error ◽  
Mathematical Models ◽  
Mean Square Error ◽  
Hybrid Model ◽  
Process Parameters ◽  
Hot Rolling ◽  
Mean Square ◽  
Roll Force ◽  
Rolled Steel

Mathematical Modeling is an effective technique for prediction of process parameters in industrial processes. Artificial Neural Network (ANN) technique has also been used to recognize a pattern in the given data by training itself. Further the trained network is used for future prediction of process parameters on the basis of the pattern recognition. The mathematical models are based on some ideal assumptions which are not valid in practical industrial conditions. Similarly high variability in industrial data makes pattern recognition difficult for ANN models and leads to high errors of prediction. In the present work, an attempt has been made to develop a hybrid model by integrating two mathematical models and ANN model for prediction of roll force during hot rolling of flat rolled steel products. The mathematical equations for roll force have been derived from the pressure distribution equation derived by Sims and Tselikov. A feed-forward network with back-propagation algorithm has been selected for ANN. All the three methods have been converted into computer code using Visual Basic.Net programming language. The hybrid model has been trained with about 2500 hot rolled steel coil data collected from Bokaro Steel Plant and Rourkela Steel Plant consisting of three different steel grades. The hybrid model has been validated with measured data of about 1000 coils. Combinations of ANN network in hybrid model having different number of hidden neurons and learning rate have been formulated, trained and validated. The final hybrid model has been selected from these combinations which has maximum accuracy. Also Multi-variable optimization technique can be used to find out the values for various input conditions which affect the flow stress and the roll force, minimizing the Root Mean Square Error. When comparing the root mean square error (RMSE) of model, it has been found that the RMSE of hybrid model is about 25% less than that of Mathematical Model.

scholarly journals Forecasting PM2.5 Concentration Using a Single-Dense Layer BiLSTM Method

Electronics ◽  
2021 ◽  
Vol 10 (15) ◽  
pp. 1808
Author(s):  
Aji Teguh Prihatno ◽  
Himawan Nurcahyanto ◽  
Md. Faisal Ahmed ◽  
Md. Habibur Rahman ◽  
Md. Morshed Alam ◽  
...  
Keyword(s):  
Mean Square Error ◽  
Time Series Data ◽  
Short Term Memory ◽  
Series Data ◽  
Percentage Error ◽  
Dense Layer ◽  
Mean Square ◽  
Time Data ◽  
Industrial Grade ◽  
Pm2.5 Concentration

In recent times, particulate matter (PM2.5) is one of the most critical air quality contaminants, and the rise of its concentration will intensify the hazard of cleanrooms. The forecasting of the concentration of PM2.5 has great importance to improve the safety of the highly pollutant-sensitive electronic circuits in the factories, especially inside semiconductor industries. In this paper, a Single-Dense Layer Bidirectional Long Short-term Memory (BiLSTM) model is developed to forecast the PM2.5 concentrations in the indoor environment by using the time series data. The real-time data samples of PM2.5 concentrations were obtained by using an industrial-grade sensor based on edge computing. The proposed model provided the best results comparing with the other existing models in terms of mean absolute error, mean square error, root mean square error, and mean absolute percentage error. These results show that the low error of forecasting PM2.5 concentration in a cleanroom in a semiconductor factory using the proposed Single-Dense Layer BiLSTM method is considerably high.

scholarly journals Tuning Deep Neural Networks for Predicting Energy Consumption in Arid Climate Based on Buildings Characteristics

2021 ◽  
Vol 13 (22) ◽  
pp. 12442
Author(s):  
Amal A. Al-Shargabi ◽  
Abdulbasit Almhafdy ◽  
Dina M. Ibrahim ◽  
Manal Alghieth ◽  
Francisco Chiclana
Keyword(s):  
Neural Networks ◽  
Energy Consumption ◽  
Mean Square Error ◽  
Deep Neural Networks ◽  
Prediction Models ◽  
Residential Buildings ◽  
Arid Climate ◽  
Mean Square ◽  
Heating And Cooling ◽  
Cooling Loads

The dramatic growth in the number of buildings worldwide has led to an increase interest in predicting energy consumption, especially for the case of residential buildings. As the heating and cooling system highly affect the operation cost of buildings; it is worth investigating the development of models to predict the heating and cooling loads of buildings. In contrast to the majority of the existing related studies, which are based on historical energy consumption data, this study considers building characteristics, such as area and floor height, to develop prediction models of heating and cooling loads. In particular, this study proposes deep neural networks models based on several hyper-parameters: the number of hidden layers, the number of neurons in each layer, and the learning algorithm. The tuned models are constructed using a dataset generated with the Integrated Environmental Solutions Virtual Environment (IESVE) simulation software for the city of Buraydah city, the capital of the Qassim region in Saudi Arabia. The Qassim region was selected because of its harsh arid climate of extremely cold winters and hot summers, which means that lot of energy is used up for cooling and heating of residential buildings. Through model tuning, optimal parameters of deep learning models are determined using the following performance measures: Mean Square Error (MSE), Root Mean Square Error (RMSE), Regression (R) values, and coefficient of determination (R2). The results obtained with the five-layer deep neural network model, with 20 neurons in each layer and the Levenberg–Marquardt algorithm, outperformed the results of the other models with a lower number of layers. This model achieved MSE of 0.0075, RMSE 0.087, R and R2 both as high as 0.99 in predicting the heating load and MSE of 0.245, RMSE of 0.495, R and R2 both as high as 0.99 in predicting the cooling load. As the developed prediction models were based on buildings characteristics, the outcomes of the research may be relevant to architects at the pre-design stage of heating and cooling energy-efficient buildings.

scholarly journals Estimated Date of Delivery with Electronic Medical Records by a Hybrid GBDT-GRU Model

2021 ◽  
Author(s):  
Yina Wu ◽  
Yichao Zhang ◽  
Xu Zou ◽  
Zhenming Yuan ◽  
Wensheng Hu ◽  
...  
Keyword(s):  
Pregnant Women ◽  
Mean Square Error ◽  
Electronic Medical Records ◽  
Hybrid Model ◽  
Medical Records ◽  
Prediction Models ◽  
Coefficient Of Determination ◽  
Gradient Boosting ◽  
Prediction Methods ◽  
Mean Square

Abstract Background: An accurate estimated date of delivery (EDD) helps pregnant women make adequate preparations before delivery and avoid the panic of parturition. EDD is normally derived from some formulates or estimated by doctors based on last menstruation period and ultrasound examinations. The main aim of this study was to develop a hybrid model to improve the accuracy of EDD and promote the health and safety of pregnant women and fetuses. Methods: This study attempted to combine antenatal examinations and electronic medical records to develop a hybrid model based on Gradient Boosting Decision Tree and Gated Recurrent Unit (GBDT-GRU). Besides exploring the features that affect the EDD, GBDT-GRU model obtained the results by dynamic prediction of different stages. The mean square error (MSE), mean absolute error (MAE) and coefficient of determination (R2) were used to compare the performance among the different prediction methods. In addition, we evaluated predictive performances of different prediction models by comparing the proportion of pregnant women under the error of different days. Results: The clinical data were collected with 33,222 pregnancy examination records from 5537 Chinese pregnant women who have given birth. Experimental results showed that the hybrid GBDT-GRU model outperformed other prediction methods with coefficient of determination (R2) of 0.84, mean square error (MSE) of 41.73. We also found that the GBDT-GRU model had a smaller deviation by comparing the bias between the actual delivery date and the EDD under different methods. Conclusions: In comparison with other prediction methods, the GBDT-GRU model provided better performance results. The results of this study are helpful for the development of guidelines for clinical delivery treatments, as it can assist clinicians in making correct decisions during obstetric examinations.

scholarly journals Data Ridden Pilots Based Estimation of Sparse Multipath Massive MIMO Channels Using Orthogonal Matching Pursuit

2019 ◽  
Vol 25 (4) ◽  
pp. 81-87 ◽  
Author(s):  
Babar Mansoor ◽  
Moazzam Islam Tiwana ◽  
Syed Junaid Nawaz ◽  
Abrar Ahmed ◽  
Abdul Haseeb ◽  
...  
Keyword(s):  
Mean Square Error ◽  
Massive Mimo ◽  
Mimo Systems ◽  
Matching Pursuit ◽  
Minimum Mean Square Error ◽  
Research Work ◽  
Base Station ◽  
Orthogonal Matching Pursuit ◽  
Mean Square ◽  
Mimo Channels

Massive Multiple-Input Multiple-Output (MIMO) is envisioned to be a strong candidate technology for the upcoming 5th generation (5G) of wireless communication networks. This research work presents a novel Compressed Sensing (CS) and Superimposed Training (SiT) based technique for estimating the sparse uplink channels in massive MIMO systems. The proposed technique involves arithmetic addition of a periodic, but low powered training sequence with each user’s information sequence. Consequently, separately dedicated resources for the pilot symbols are not needed. Moreover, to attain the estimates of the Channel State Information (CSI) in the uplink, the sparsity exhibited by the MIMO channels is exploited by incorporating CS based Orthogonal Matching Pursuit (OMP) algorithm. For decoding the transmitted information symbols of each user, a Linear Minimum Mean Square Error (LMMSE) based equalizer is incorporated at the receiving Base Station (BS). Based on the obtained simulation results, the proposed SiT-OMP technique outperforms the existing Least Squares (SiT) channel estimation technique. The comparison is done using performance metrics of the Bit Error Rate (BER) and the Normalized Channel Mean Square Error (NCMSE).

scholarly journals Estimated Date of Delivery With Electronic Medical Records By A Hybrid GBDT-GRU Model

2021 ◽  
Author(s):  
Yina Wu ◽  
Yichao Zhang ◽  
Xu Zou ◽  
Zhenming Yuan ◽  
Wensheng Hu ◽  
...  
Keyword(s):  
Pregnant Women ◽  
Mean Square Error ◽  
Electronic Medical Records ◽  
Hybrid Model ◽  
Medical Records ◽  
Prediction Models ◽  
Coefficient Of Determination ◽  
Gradient Boosting ◽  
Prediction Methods ◽  
Mean Square

Abstract An accurate estimated date of delivery (EDD) helps pregnant women make adequate preparations before delivery and avoid the panic of parturition. EDD is normally derived from some formulates or estimated by doctors based on last menstruation period and ultrasound examinations. The main aim of this study was to develop a hybrid model to improve the accuracy of EDD and promote the health and safety of pregnant women and fetuses. This study attempted to combine antenatal examinations and electronic medical records to develop a hybrid model based on Gradient Boosting Decision Tree and Gated Recurrent Unit (GBDT-GRU). Besides exploring the features that affect the EDD, GBDT-GRU model obtained the results by dynamic prediction of different stages. The mean square error (MSE) and coefficient of determination (R2) were used to compare the performance among the different prediction methods. In addition, we evaluated predictive performances of different prediction models by comparing the proportion of pregnant women under the error of different days. The clinical data were collected with 33,222 pregnancy examination records from 5537 Chinese pregnant women who have given birth. Experimental results showed that the hybrid GBDT-GRU model outperformed other prediction methods with coefficient of determination (R2) of 0.84, mean square error (MSE) of 41.73. We also found that the GBDT-GRU model had a smaller deviation by comparing the bias between the actual delivery date and the EDD under different methods. In comparison with other prediction methods, the GBDT-GRU model provided better performance results. The results of this study are helpful for the development of guidelines for clinical delivery treatments, as it can assist clinicians in making correct decisions during obstetric examinations.

scholarly journals Prediction in a Hybrid of Fuzzy Linear Regression with Symmetric Parameter Model and Fuzzy C-Means Method Using Simulation Data

2018 ◽  
Vol 7 (4.30) ◽  
pp. 419
Author(s):  
Muhammad Ammar Shafi ◽  
Mohd Saifullah Rusiman ◽  
Kavikumar Jacob ◽  
Nor Shamsidah Amir Hamzah ◽  
Norziha Che Him ◽  
...  
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Mean Square Error ◽  
Hybrid Model ◽  
Linear Regression Model ◽  
Measurement Errors ◽  
Mean Square ◽  
Fuzzy Linear Regression ◽  
Simulation Data ◽  
Fuzzy Linear Regression Model

The objective of fuzzy linear regression model (FLRM) to predict the dependent variable and independent variables in vague phenomenon. In this study, several models such as fuzzy linear regression model (FLRM), fuzzy linear regression with symmetric parameter (FLWSP) and a hybrid model have been applied to be evaluated by 1000 rows in 1 simulation data. Moreover, the hybrid method was applied between fuzzy linear regression with symmetric parameter (FLRWSP) and fuzzy c-mean (FCM) method to get the effective prediction in a new model and best result in this study. To improve the accuracy of evaluating and predicting, this study employ two measurement error of cross validation statistical technique which are mean square error (MSE) and root mean square error (RMSE). The simulation result suggests that comparison among models using two measurement errors should be to determine the best results. Finally, this study notes that the new hybrid model of FLRWSP and FCM is verified to be a good model with the least value of MSE and RMSE measurement errors.

Sign in / Sign up

Export Citation Format

Share Document