Long Short-Term Memory-Based Model Approach for Energy Consumption Prediction to Trigger High Consumption Alert

2019 ◽  
Author(s):  
Nur Athirah Rosli ◽  
Norliza Zaini ◽  
Mohd Fuad Abdul Latip
Keyword(s):  
Energy Consumption ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
High Consumption ◽  
Long Short Term Memory ◽  
Energy Consumption Prediction ◽  
Consumption Prediction ◽  
Model Approach

scholarly journals Sequential Learning-Based Energy Consumption Prediction Model for Residential and Commercial Sectors

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 605
Author(s):  
Ijaz Ul Haq ◽  
Amin Ullah ◽  
Samee Ullah Khan ◽  
Noman Khan ◽  
Mi Young Lee ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Prediction Model ◽  
Short Term Memory ◽  
Electrical Energy ◽  
Sequential Learning ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Energy Consumption Prediction ◽  
Consumption Prediction

The use of electrical energy is directly proportional to the increase in global population, both concerning growing industrialization and rising residential demand. The need to achieve a balance between electrical energy production and consumption inspires researchers to develop forecasting models for optimal and economical energy use. Mostly, the residential and industrial sectors use metering sensors that only measure the consumed energy but are unable to manage electricity. In this paper, we present a comparative analysis of a variety of deep features with several sequential learning models to select the optimized hybrid architecture for energy consumption prediction. The best results are achieved using convolutional long short-term memory (ConvLSTM) integrated with bidirectional long short-term memory (BiLSTM). The ConvLSTM initially extracts features from the input data to produce encoded sequences that are decoded by BiLSTM and then proceeds with a final dense layer for energy consumption prediction. The overall framework consists of preprocessing raw data, extracting features, training the sequential model, and then evaluating it. The proposed energy consumption prediction model outperforms existing models over publicly available datasets, including Household and Korean commercial building datasets.

A novel fuzzy rough set based long short-term memory integration model for energy consumption prediction of public buildings

2020 ◽  
pp. 1-15
Author(s):  
Hongchang Sun ◽  
Yadong wang ◽  
Lanqiang Niu ◽  
Fengyu Zhou ◽  
Heng Li
Keyword(s):  
Energy Consumption ◽  
Rough Set ◽  
Prediction Accuracy ◽  
Short Term Memory ◽  
Short Term ◽  
Fuzzy Rough Set ◽  
Proposed Model ◽  
Long Short Term Memory ◽  
Energy Consumption Prediction ◽  
Consumption Prediction

Building energy consumption (BEC) prediction is very important for energy management and conservation. This paper presents a short-term energy consumption prediction method that integrates the Fuzzy Rough Set (FRS) theory and the Long Short-Term Memory (LSTM) model, and is thus named FRS-LSTM. This method can find the most directly related factors from the complex and diverse factors influencing the energy consumption, which improves the prediction accuracy and efficiency. First, the FRS is used to reduce the redundancy of the input features by the attribute reduction of the factors affecting the energy consumption forecasting, and solves the data loss problem caused by the data discretization of a classical rough set. Then, the final attribute set after reduction is taken as the input of the LSTM networks to obtain the final prediction results. To validate the effectiveness of the proposed model, this study used the actual data of a public building to predict the building’s energy consumption, and compared the proposed model with the LSTM, Levenberg-Marquardt Back Propagation (LM-BP), and Support Vector Regression (SVR) models. The experimental results reveal that the presented FRS-LSTM model achieves higher prediction accuracy compared with other comparative models.

scholarly journals Long Short-Term Memory Network-Based Metaheuristic for Effective Electric Energy Consumption Prediction

2021 ◽  
Vol 11 (23) ◽  
pp. 11263
Author(s):  
Simran Kaur Hora ◽  
Rachana Poongodan ◽  
Rocío Pérez de Prado ◽  
Marcin Wozniak ◽  
Parameshachari Bidare Divakarachari
Keyword(s):  
Energy Management ◽  
Short Term Memory ◽  
Electric Energy ◽  
Short Term ◽  
Term Memory ◽  
Electric Energy Consumption ◽  
Long Short Term Memory ◽  
Lstm Network ◽  
Energy Consumption Prediction ◽  
Consumption Prediction

The Electric Energy Consumption Prediction (EECP) is a complex and important process in an intelligent energy management system and its importance has been increasing rapidly due to technological developments and human population growth. A reliable and accurate model for EECP is considered a key factor for an appropriate energy management policy. In recent periods, many artificial intelligence-based models have been developed to perform different simulation functions, engineering techniques, and optimal energy forecasting in order to predict future energy demands on the basis of historical data. In this article, a new metaheuristic based on a Long Short-Term Memory (LSTM) network model is proposed for an effective EECP. After collecting data sequences from the Individual Household Electric Power Consumption (IHEPC) dataset and Appliances Load Prediction (AEP) dataset, data refinement is accomplished using min-max and standard transformation methods. Then, the LSTM network with Butterfly Optimization Algorithm (BOA) is developed for EECP. In this article, the BOA is used to select optimal hyperparametric values which precisely describe the EEC patterns and discover the time series dynamics in the energy domain. This extensive experiment conducted on the IHEPC and AEP datasets shows that the proposed model obtains a minimum error rate relative to the existing models.

scholarly journals A Hybrid Model for Power Consumption Forecasting Using VMD-Based the Long Short-Term Memory Neural Network

2022 ◽  
Vol 9 ◽  
Author(s):  
Yingjun Ruan ◽  
Gang Wang ◽  
Hua Meng ◽  
Fanyue Qian
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Power Consumption ◽  
Hybrid Model ◽  
Short Term Memory ◽  
Series Data ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Individual Features

Energy consumption prediction is a popular research field in computational intelligence. However, it is difficult for general machine learning models to handle complex time series data such as building energy consumption data, and the results are often unsatisfactory. To address this difficulty, a hybrid prediction model based on modal decomposition was proposed in this paper. For data preprocessing, the variational mode decomposition (VMD) technique was used to used to decompose the original sequence into more robust subsequences. In the feature selection, the maximum relevance minimum redundancy (mRMR) algorithm was chosen to analyse the correlation between each component and the individual features while eliminating the redundancy between individual features. In the forecasting module, the long short-term memory (LSTM) neural network model was used to predict power consumption. In order to verify the performance of the proposed model, three categories of contrast methods were applied: 1) Comparing the hybrid model to a single predictive model, 2) Comparing the hybrid model with the backpropagation neural network (BPNN) to the hybrid model with the LSTM and 3) Comparing the hybrid model using mRMR and the hybrid model using mutual information maximization (MIM). The experimental results on the measured data of an office building in Qingdao show that the proposed hybrid model can improve the prediction accuracy and has better robustness compared to VMD-MIM-LSTM. In the three control groups mentioned above, the R2 value of the hybrid model improved by 10, 3 and 3%, respectively, the values of the mean absolute error (MAE) decreased by 48.9, 41.4 and 35.6%, respectively, and the root mean square error (RMSE) decreased by 54.7, 35.5 and 34.1%, respectively.

scholarly journals Improving Electric Energy Consumption Prediction Using CNN and Bi-LSTM

2019 ◽  
Vol 9 (20) ◽  
pp. 4237 ◽  
Author(s):  
Tuong Le ◽  
Minh Thanh Vo ◽  
Bay Vo ◽  
Eenjun Hwang ◽  
Seungmin Rho ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Performance Metrics ◽  
Short Term Memory ◽  
State Of The Art ◽  
Electric Energy ◽  
The State ◽  
Short Term ◽  
Electric Energy Consumption ◽  
Energy Consumption Prediction ◽  
Consumption Prediction

The electric energy consumption prediction (EECP) is an essential and complex task in intelligent power management system. EECP plays a significant role in drawing up a national energy development policy. Therefore, this study proposes an Electric Energy Consumption Prediction model utilizing the combination of Convolutional Neural Network (CNN) and Bi-directional Long Short-Term Memory (Bi-LSTM) that is named EECP-CBL model to predict electric energy consumption. In this framework, two CNNs in the first module extract the important information from several variables in the individual household electric power consumption (IHEPC) dataset. Then, Bi-LSTM module with two Bi-LSTM layers uses the above information as well as the trends of time series in two directions including the forward and backward states to make predictions. The obtained values in the Bi-LSTM module will be passed to the last module that consists of two fully connected layers for finally predicting the electric energy consumption in the future. The experiments were conducted to compare the prediction performances of the proposed model and the state-of-the-art models for the IHEPC dataset with several variants. The experimental results indicate that EECP-CBL framework outperforms the state-of-the-art approaches in terms of several performance metrics for electric energy consumption prediction on several variations of IHEPC dataset in real-time, short-term, medium-term and long-term timespans.

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