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

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.

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

2019 IEEE 9th International Conference on System Engineering and Technology (ICSET) ◽

10.1109/icsengt.2019.8906335 ◽

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

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

Mathematics ◽

10.3390/math9060605 ◽

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.

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Forecasting Day-Ahead Hourly Photovoltaic Power Generation Using Convolutional Self-Attention Based Long Short-Term Memory

Energies ◽

10.3390/en13154017 ◽

2020 ◽

Vol 13 (15) ◽

pp. 4017 ◽

Cited By ~ 2

Author(s):

Dukhwan Yu ◽

Wonik Choi ◽

Myoungsoo Kim ◽

Ling Liu

Keyword(s):

Power Generation ◽

Deep Learning ◽

Prediction Accuracy ◽

Short Term Memory ◽

Local Context ◽

Short Term ◽

Term Memory ◽

Proposed Model ◽

Long Short Term Memory ◽

Pv Power Generation

The problem of Photovoltaic (PV) power generation forecasting is becoming crucial as the penetration level of Distributed Energy Resources (DERs) increases in microgrids and Virtual Power Plants (VPPs). In order to improve the stability of power systems, a fair amount of research has been proposed for increasing prediction performance in practical environments through statistical, machine learning, deep learning, and hybrid approaches. Despite these efforts, the problem of forecasting PV power generation remains to be challenging in power system operations since existing methods show limited accuracy and thus are not sufficiently practical enough to be widely deployed. Many existing methods using long historical data suffer from the long-term dependency problem and are not able to produce high prediction accuracy due to their failure to fully utilize all features of long sequence inputs. To address this problem, we propose a deep learning-based PV power generation forecasting model called Convolutional Self-Attention based Long Short-Term Memory (LSTM). By using the convolutional self-attention mechanism, we can significantly improve prediction accuracy by capturing the local context of the data and generating keys and queries that fit the local context. To validate the applicability of the proposed model, we conduct extensive experiments on both PV power generation forecasting using a real world dataset and power consumption forecasting. The experimental results of power generation forecasting using the real world datasets show that the MAPEs of the proposed model are much lower, in fact by 7.7%, 6%, 3.9% compared to the Deep Neural Network (DNN), LSTM and LSTM with the canonical self-attention, respectively. As for power consumption forecasting, the proposed model exhibits 32%, 17% and 44% lower Mean Absolute Percentage Error (MAPE) than the DNN, LSTM and LSTM with the canonical self-attention, respectively.

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Improving Electric Energy Consumption Prediction Using CNN and Bi-LSTM

Applied Sciences ◽

10.3390/app9204237 ◽

2019 ◽

Vol 9 (20) ◽

pp. 4237 ◽

Cited By ~ 17

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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Attention-Based Long Short-Term Memory Recurrent Neural Network for Capacity Degradation of Lithium-Ion Batteries

Batteries ◽

10.3390/batteries7040066 ◽

2021 ◽

Vol 7 (4) ◽

pp. 66

Author(s):

Tadele Mamo ◽

Fu-Kwun Wang

Keyword(s):

Neural Network ◽

Recurrent Neural Network ◽

Prediction Accuracy ◽

Short Term Memory ◽

Lithium Ion ◽

Short Term ◽

Term Memory ◽

Proposed Model ◽

Capacity Degradation ◽

Long Short Term Memory

Monitoring cycle life can provide a prediction of the remaining battery life. To improve the prediction accuracy of lithium-ion battery capacity degradation, we propose a hybrid long short-term memory recurrent neural network model with an attention mechanism. The hyper-parameters of the proposed model are also optimized by a differential evolution algorithm. Using public battery datasets, the proposed model is compared to some published models, and it gives better prediction performance in terms of mean absolute percentage error and root mean square error. In addition, the proposed model can achieve higher prediction accuracy of battery end of life.

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Intelligent Prediction Method of Building Energy Consumption Based on Deep Learning

Scientific Programming ◽

10.1155/2021/3323316 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Bingqian Fan ◽

Xuanxuan Xing

Keyword(s):

Deep Learning ◽

Energy Consumption ◽

Prediction Model ◽

Prediction Accuracy ◽

Simulation Experiment ◽

Short Term Memory ◽

Building Energy ◽

Building Energy Consumption ◽

Energy Consumption Prediction ◽

Consumption Prediction

Building energy consumption prediction plays an important role in realizing building energy conservation control. Limited by some external factors such as temperature, there are some problems in practical applications, such as complex operation and low prediction accuracy. Aiming at the problem of low prediction accuracy caused by poor timing of existing building energy consumption prediction methods, a building energy consumption prediction and analysis method based on the deep learning network is proposed in this paper. Before establishing the energy consumption prediction model, the building energy consumption data source is preprocessed and analyzed. Then, based on the Keras deep learning framework, an improved long short-term memory (ILSTM) prediction model is built to support the accurate analysis of the whole cycle of the prediction network. At the same time, the adaptive moment (Adam) estimation algorithm is used to update and optimize the weight parameters of the model to realize the adaptive and rapid update and matching of network parameters. The simulation experiment is based on the actual dataset collected by a university in Southwest China. The experimental results show that the evaluation indexes MAE and RMSE of the proposed method are 0.015 and 0.109, respectively, which are better than the comparison method. The simulation experiment proves that the proposed method is feasible.

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

Applied Sciences ◽

10.3390/app112311263 ◽

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.

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A Hybrid Model for Short-Term Traffic Flow Prediction Based on Variational Mode Decomposition, Wavelet Threshold Denoising, and Long Short-Term Memory Neural Network

Complexity ◽

10.1155/2021/7756299 ◽

2021 ◽

Vol 2021 ◽

pp. 1-24

Author(s):

Yang Yu ◽

Qiang Shang ◽

Tian Xie

Keyword(s):

Traffic Flow ◽

Prediction Accuracy ◽

Short Term Memory ◽

Short Term ◽

Term Memory ◽

Traffic Flow Prediction ◽

Flow Prediction ◽

Mode Decomposition ◽

Proposed Model ◽

Long Short Term Memory

Traffic flow prediction plays an important role in intelligent transportation system (ITS). However, due to the randomness and complex periodicity of traffic flow data, traditional prediction models often fail to achieve good results. On the other hand, external disturbances or abnormal detectors will cause the collected traffic flow data to contain noise components, resulting in a decrease in prediction accuracy. In order to improve the accuracy of traffic flow prediction, this study proposes a mixed traffic flow prediction model VMD-WD-LSTM using variational mode decomposition (VMD), wavelet threshold denoising (WD), and long short-term memory (LSTM) network. Firstly, we decompose the original traffic flow sequence into K components through VMD and determine the number of components K according to the sample entropy of different K values. Then, each component is denoised by wavelet threshold to obtain the denoised subsequence. Finally, LSTM is used to predict each subsequence, and the predicted values of each subsequence are combined into the final prediction results. In addition, the performance of the proposed model and the latest traffic flow prediction model is compared on the several well-known public datasets. The empirical analysis shows that the proposed model not only has good prediction accuracy but also has superior robustness.

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Scheduling Sensor Duty Cycling Based on Event Detection Using Bi-Directional Long Short-Term Memory and Reinforcement Learning

Sensors ◽

10.3390/s20195498 ◽

2020 ◽

Vol 20 (19) ◽

pp. 5498

Author(s):

Muhammad Diyan ◽

Murad Khan ◽

Bhagya Nathali Silva ◽

Kijun Han

Keyword(s):

Energy Consumption ◽

Smart Home ◽

Short Term Memory ◽

Detection Accuracy ◽

Activity Detection ◽

Short Term ◽

Duty Cycling ◽

Term Memory ◽

Proposed Model ◽

Long Short Term Memory

A smart home provides a facilitated environment for the detection of human activity with appropriate Deep Learning algorithms to manipulate data collected from numerous sensors attached to various smart things in a smart home environment. Human activities comprise expected and unexpected behavior events; therefore, detecting these events consisting of mutual dependent activities poses a key challenge in the activities detection paradigm. Besides, the battery-powered sensor ubiquitously and extensively monitors activities, disputes, and sensor energy depletion. Therefore, to address these challenges, we propose an Energy and Event Aware-Sensor Duty Cycling scheme. The proposed model predicts the future expected event using the Bi-Directional Long-Short Term Memory model and allocates Predictive Sensors to the predicted event. To detect the unexpected events, the proposed model localizes a Monitor Sensor within a cluster of Hibernate Sensors using the Jaccard Similarity Index. Finally, we optimize the performance of our proposed scheme by employing the Q-Learning algorithm to track the missed or undetected events. The simulation is executed against the conventional Machine Learning algorithms for the sensor duty cycle, scheduling to reduce the sensor energy consumption and improve the activity detection accuracy. The experimental evaluation of our proposed scheme shows significant improvement in activity detection accuracy from 94.12% to 96.12%. Besides, the effective rotation of the Monitor Sensor significantly improves the energy consumption of each sensor with the entire network lifetime.

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