A hybrid model for building energy consumption forecasting using long short term memory networks

Somu, Nivethitha; R, Gauthama Raman M; Ramamritham, Krithi

doi:10.1016/j.apenergy.2019.114131

Cited by 271 publications

(90 citation statements)

References 44 publications

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“…Similarly, Aslam et al [34] developed a trust-worthy energy management system by utilizing mixed-integer linear programming (MILP) and also established a friendly environment between consumers and energy generation. Bourhnane et al [35] presented a model for energy forecasting and scheduling in smart buildings by integrating artificial neural network (ANN) and genetic algorithms. Further, they also tested the model in real-time, which produced incredible output for both short-and long-term forecasting.…”

Section: Literature Reviewmentioning

confidence: 99%

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

et al. 2021

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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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Section: Literature Reviewmentioning

confidence: 99%

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

et al. 2021

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“…The cell state is regulated with three different gates (input, output, and forget gates), and the role of these gates is to control the amount of information passed between layers. The LSTM network has been notably applied in speech-to-text transcription, machine translation, process forecasting, and language modeling (Peng et al 2018;Sherstinsky 2020;Somu et al 2020). A LSTM algorithm can learn how to connect minimal time lags of more than 1000 discrete time steps.…”

Section: Long Short-term Memory (Lstm)mentioning

confidence: 99%

Applications of soft computing models for predicting sea surface temperature: a comprehensive review and assessment

Haghbin

Sharafati

Motta

et al. 2021

Prog Earth Planet Sci

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The application of soft computing (SC) models for predicting environmental variables is widely gaining popularity, because of their capability to describe complex non-linear processes. The sea surface temperature (SST) is a key quantity in the analysis of sea and ocean systems, due to its relation with water quality, organisms, and hydrological events such as droughts and floods. This paper provides a comprehensive review of the SC model applications for estimating SST over the last two decades. Types of model (based on artificial neural networks, fuzzy logic, or other SC techniques), input variables, data sources, and performance indices are discussed. Existing trends of research in this field are identified, and possible directions for future investigation are suggested.

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“…Similarly, if the LSTM model is employed to data that is already pre-processed or refined with another machine learning algorithm then it might work well. Thus, in the current literature, the LSTM model is used in concatenation with other machine learning models to produce better results [ 27 , 28 ]. In this regard, a hybrid LSTM and CNN model is presented to forecast the photovoltaic power consumption in [ 28 ].…”

Section: Related Workmentioning

confidence: 99%

Towards Energy Efficient Home Automation: A Deep Learning Approach

Khan

Seo

Kim

2020

Sensors

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Home Automation Systems (HAS) attracted much attention during the last decade due to the developments in new wireless technologies, such as Bluetooth 4.0, 5G, WiFi 6, etc. In order to enable automation as a service in smart homes, a number of challenges must be addressed, such as fulfilling the electrical energy demands, scheduling the operational time of appliances, applying machine learning models in real-time, optimal human appliances interaction, etc. In order to address the aforementioned challenges and control the wastage of energy due to the lifestyle of the home users, we propose a system for automatically controlling the energy consumption by employing machine and deep learning techniques to smart home networks. The proposed system works in three phases, (1) feature extraction and classification based on 1-dimensional Deep Convolutional Neural Network (1D-DCNN) which extract important energy patterns from the historic energy data, (2) a load forecasting system based on Long-short Term Memory (LSTM) is proposed to forecast the load based on the extracted features in phase 1 and (3) a scheduling algorithm based on the forecasted data obtained from phase 2 is designed to schedule the operational time of smart home appliances. The proposed scheme efficiently automates the smart home appliances to consume less energy while adapting to the lifestyle of smart home users. The validation of the proposed scheme is tested with a number of simulation scenarios incorporating datasets from authentic data sources. The simulation results show that the proposed smart home automation system can be a game-changer in fulfilling the energy demands of the home users without installing renewable and other energy sources in the future.

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A hybrid model for building energy consumption forecasting using long short term memory networks

Cited by 271 publications

References 44 publications

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

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

Applications of soft computing models for predicting sea surface temperature: a comprehensive review and assessment

Towards Energy Efficient Home Automation: A Deep Learning Approach

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