scholarly journals Multiple Electric Energy Consumption Forecasting Using a Cluster-Based Strategy for Transfer Learning in Smart Building

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2668 ◽  
Author(s):  
Tuong Le ◽  
Minh Thanh Vo ◽  
Tung Kieu ◽  
Eenjun Hwang ◽  
Seungmin Rho ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Transfer Learning ◽  
Energy Management ◽  
Short Term Memory ◽  
Electric Energy ◽  
Computational Time ◽  
Short Term ◽  
Smart Building ◽  
Electric Energy Consumption ◽  
Long Short Term Memory

Electric energy consumption forecasting is an interesting, challenging, and important issue in energy management and equipment efficiency improvement. Existing approaches are predictive models that have the ability to predict for a specific profile, i.e., a time series of a whole building or an individual household in a smart building. In practice, there are many profiles in each smart building, which leads to time-consuming and expensive system resources. Therefore, this study develops a robust framework for the Multiple Electric Energy Consumption forecasting (MEC) of a smart building using Transfer Learning and Long Short-Term Memory (TLL), the so-called MEC-TLL framework. In this framework, we first employ a k-means clustering algorithm to cluster the daily load demand of many profiles in the training set. In this phase, we also perform Silhouette analysis to specify the optimal number of clusters for the experimental datasets. Next, this study develops the MEC training algorithm, which utilizes a cluster-based strategy for transfer learning the Long Short-Term Memory models to reduce the computational time. Finally, extensive experiments are conducted to compare the computational time and different performance metrics for multiple electric energy consumption forecasting on two smart buildings in South Korea. The experimental results indicate that our proposed approach is capable of economical overheads while achieving superior performances. Therefore, the proposed approach can be applied effectively for intelligent energy management in smart buildings.

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 Forecasting Energy Consumption of Wastewater Treatment Plants with a Transfer Learning Approach for Sustainable Cities

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1149
Author(s):  
Pedro Oliveira ◽  
Bruno Fernandes ◽  
Cesar Analide ◽  
Paulo Novais
Keyword(s):  
Wastewater Treatment ◽  
Energy Consumption ◽  
Transfer Learning ◽  
Short Term Memory ◽  
Wastewater Treatment Plants ◽  
Learning Approach ◽  
Sustainable Cities ◽  
Short Term ◽  
Long Short Term Memory ◽  
Gated Recurrent Units

A major challenge of today’s society is to make large urban centres more sustainable. Improving the energy efficiency of the various infrastructures that make up cities is one aspect being considered when improving their sustainability, with Wastewater Treatment Plants (WWTPs) being one of them. Consequently, this study aims to conceive, tune, and evaluate a set of candidate deep learning models with the goal being to forecast the energy consumption of a WWTP, following a recursive multi-step approach. Three distinct types of models were experimented, in particular, Long Short-Term Memory networks (LSTMs), Gated Recurrent Units (GRUs), and uni-dimensional Convolutional Neural Networks (CNNs). Uni- and multi-variate settings were evaluated, as well as different methods for handling outliers. Promising forecasting results were obtained by CNN-based models, being this difference statistically significant when compared to LSTMs and GRUs, with the best model presenting an approximate overall error of 630 kWh when on a multi-variate setting. Finally, to overcome the problem of data scarcity in WWTPs, transfer learning processes were implemented, with promising results being achieved when using a pre-trained uni-variate CNN model, with the overall error reducing to 325 kWh.

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.

scholarly journals Efficient Energy Management Based on Convolutional Long Short-Term Memory Network for Smart Power Distribution System

Energies ◽  
2021 ◽  
Vol 14 (19) ◽  
pp. 6161
Author(s):  
Faisal Mohammad ◽  
Mohamed A. Ahmed ◽  
Young-Chon Kim
Keyword(s):  
Energy Consumption ◽  
Energy Management ◽  
Short Term Memory ◽  
Spatiotemporal Pattern ◽  
Sequential Dependency ◽  
Short Term ◽  
Term Memory ◽  
Power Distribution System ◽  
Efficient Energy ◽  
Long Short Term Memory

An efficient energy management system is integrated with the power grid to collect information about the energy consumption and provide the appropriate control to optimize the supply–demand pattern. Therefore, there is a need for intelligent decisions for the generation and distribution of energy, which is only possible by making the correct future predictions. In the energy market, future knowledge of the energy consumption pattern helps the end-user to decide when to buy or sell the energy to reduce the energy cost and decrease the peak consumption. The Internet of things (IoT) and energy data analytic techniques have provided the convenience to collect the data from the end devices on a large scale and to manipulate all the recorded data. Forecasting an electric load is fairly challenging due to the high uncertainty and dynamic nature involved due to spatiotemporal pattern consumption. Existing conventional forecasting models lack the ability to deal with the spatio-temporally varying data. To overcome the above-mentioned challenges, this work proposes an encoder–decoder model based on convolutional long short-term memory networks (ConvLSTM) for energy load forecasting. The proposed architecture uses encode consisting of multiple ConvLSTM layers to extract the salient features in the data and to learn the sequential dependency and then passes the output to the decoder, having LSTM layers to make forecasting. The forecasting results produced by the proposed approach are favorably comparable to the existing state-of-the-art and better than the conventional methods with the least error rate. Quantitative analyses show that a mean absolute percentage error (MAPE) of 6.966% for household energy consumption and 16.81% for city-wide energy consumption is obtained for the proposed forecasting model in comparison with existing encoder–decoder-based deep learning models for two real-world datasets.

scholarly journals Intelligent Islanding Detection of Microgrids Using Long Short-Term Memory Networks

Energies ◽  
2021 ◽  
Vol 14 (18) ◽  
pp. 5762
Author(s):  
Syed Basit Ali Bukhari ◽  
Khawaja Khalid Mehmood ◽  
Abdul Wadood ◽  
Herie Park
Keyword(s):  
Short Term Memory ◽  
Computational Time ◽  
Islanding Detection ◽  
Phase Voltage ◽  
Short Term ◽  
Term Memory ◽  
Three Phase ◽  
Empirical Wavelet Transform ◽  
Long Short Term Memory ◽  
Lstm Network

This paper presents a new intelligent islanding detection scheme (IIDS) based on empirical wavelet transform (EWT) and long short-term memory (LSTM) network to identify islanding events in microgrids. The concept of EWT is extended to extract features from three-phase signals. First, the three-phase voltage signals sampled at the terminal of targeted distributed energy resource (DER) or point of common coupling (PCC) are decomposed into empirical modes/frequency subbands using EWT. Then, instantaneous amplitudes and instantaneous frequencies of the three-phases at different frequency subbands are combined, and various statistical features are calculated. Finally, the EWT-based features along with the three-phase voltage signals are input to the LSTM network to differentiate between non-islanding and islanding events. To assess the efficacy of the proposed IIDS, extensive simulations are performed on an IEC microgrid and an IEEE 34-node system. The simulation results verify the effectiveness of the proposed IIDS in terms of non-detection zone (NDZ), computational time, detection accuracy, and robustness against noisy measurement. Furthermore, comparisons with existing intelligent methods and different LSTM architectures demonstrate that the proposed IIDS offers higher reliability by significantly reducing the NDZ and stands robust against measurements uncertainty.

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 Enhancing Machine Learning Prediction in Cybersecurity Using Dynamic Feature Selector

2021 ◽  
Vol 1 (1) ◽  
pp. 199-218
Author(s):  
Mostofa Ahsan ◽  
Rahul Gomes ◽  
Md. Minhaz Chowdhury ◽  
Kendall E. Nygard
Keyword(s):  
Machine Learning ◽  
Short Term Memory ◽  
Computational Time ◽  
Dynamic Feature ◽  
Short Term ◽  
Feature Size ◽  
Learning Stage ◽  
Term Memory ◽  
Feature Selector ◽  
Long Short Term Memory

Machine learning algorithms are becoming very efficient in intrusion detection systems with their real time response and adaptive learning process. A robust machine learning model can be deployed for anomaly detection by using a comprehensive dataset with multiple attack types. Nowadays datasets contain many attributes. Such high dimensionality of datasets poses a significant challenge to information extraction in terms of time and space complexity. Moreover, having so many attributes may be a hindrance towards creation of a decision boundary due to noise in the dataset. Large scale data with redundant or insignificant features increases the computational time and often decreases goodness of fit which is a critical issue in cybersecurity. In this research, we have proposed and implemented an efficient feature selection algorithm to filter insignificant variables. Our proposed Dynamic Feature Selector (DFS) uses statistical analysis and feature importance tests to reduce model complexity and improve prediction accuracy. To evaluate DFS, we conducted experiments on two datasets used for cybersecurity research namely Network Security Laboratory (NSL-KDD) and University of New South Wales (UNSW-NB15). In the meta-learning stage, four algorithms were compared namely Bidirectional Long Short-Term Memory (Bi-LSTM), Gated Recurrent Units, Random Forest and a proposed Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM) for accuracy estimation. For NSL-KDD, experiments revealed an increment in accuracy from 99.54% to 99.64% while reducing feature size of one-hot encoded features from 123 to 50. In UNSW-NB15 we observed an increase in accuracy from 90.98% to 92.46% while reducing feature size from 196 to 47. The proposed approach is thus able to achieve higher accuracy while significantly lowering number of features required for processing.

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