Towards Efficient Building Designing: Heating and Cooling Load Prediction via Multi-Output Model

Sajjad, Muhammad; Khan, Samee U.; Khan, Noman; Haq, Ijaz Ul; Ullah, Amin; Lee, Mi Young; Baik, Sung Wook

doi:10.3390/s20226419

Cited by 38 publications

(26 citation statements)

References 40 publications

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“…traditional and non-traditional (machine learning and artificial intelligence) have their own advantages and disadvantages. However, nowadays AI-based methods are the most popular due to their high-performance outcomes and reliability [9]. These AI-based methods such as CNN, recurrent neural network (RNN), multi-layer perceptron (MLP), and ensemble methods have been vastly used for time-series and energy forecasting problems [10].…”

Section: Introductionmentioning

confidence: 99%

Short-Term Energy Forecasting Framework Using an Ensemble Deep Learning Approach

2021

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Industrial and building sectors demand efficient smart energy strategies, techniques of optimization, and efficient management for reducing global energy consumption due to the increasing world population. Nowadays, various artificial intelligence (AI) based methods are utilized to perform optimal energy forecasting, different simulation tools, and engineering methods to predict future demand based on historical data. Nevertheless, nonlinear energy demand modeling is still unfledged for a better solution to handle short-term and long-term dependencies and avoid static nature because it is purely on historical datadriven. In this paper, we propose an ensemble deep learning-based approach to predict and forecast energy demand and consumption by using chronological dependencies. Our system initially processes the data, cleaning, normalization, and transformation to ensure the model performance. Furthermore, the preprocess data is fed to propose the ensemble model to extract hybrid discriminative features by using convolution neural network (CNN), stacked, and bi-directional long-short term memory (LSTM) architectures. We trained our proposed system on the historical data to forecast the energy demand and consumption with a different time interval. In the proposed technique, we utilized the concept of active learning based on moving windows to ensure and improve the prediction performance of the system. The proposed system could be applicable to employ energy consumption in industrial and building sectors to demonstrate their significance and effectiveness. We evaluated the proposed system by using benchmark, residential UCI, and local Korean commercial building datasets. We conducted different extensive experimentation to show the error rate and used various kinds of evaluation matrices, which indicate the lower error rate of the proposed system.

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Section: Introductionmentioning

confidence: 99%

Short-Term Energy Forecasting Framework Using an Ensemble Deep Learning Approach

2021

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“…The LSTM preserves the differential values of old inputs during backpropagation to solve the long-term dependency problem of the RNN. As a variant of the RNN architecture, GRU has the advantage of simplifying the LSTM structure by reducing the computation to update the hidden state while solving the long-term dependency problem and maintaining the performance of the LSTM [ 11 , 32 ].…”

Section: Forecasting Model Constructionmentioning

confidence: 99%

“…Recently, a gated recurrent unit (GRU) was used for STLF to solve these problems because it is simple and easy to implement and can carry out multistep-ahead forecasting [ 11 ]. However, the GRU has the disadvantage that its forecasting accuracy may deteriorate for a long input sequence because it concentrates on all variables equally.…”

Section: Introductionmentioning

confidence: 99%

An Attention-Based Multilayer GRU Model for Multistep-Ahead Short-Term Load Forecasting

Jung

Moon

Park

et al. 2021

Sensors

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Recently, multistep-ahead prediction has attracted much attention in electric load forecasting because it can deal with sudden changes in power consumption caused by various events such as fire and heat wave for a day from the present time. On the other hand, recurrent neural networks (RNNs), including long short-term memory and gated recurrent unit (GRU) networks, can reflect the previous point well to predict the current point. Due to this property, they have been widely used for multistep-ahead prediction. The GRU model is simple and easy to implement; however, its prediction performance is limited because it considers all input variables equally. In this paper, we propose a short-term load forecasting model using an attention based GRU to focus more on the crucial variables and demonstrate that this can achieve significant performance improvements, especially when the input sequence of RNN is long. Through extensive experiments, we show that the proposed model outperforms other recent multistep-ahead prediction models in the building-level power consumption forecasting.

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“…While processing the complex and long sequences using forward-to-backward forms, recurrent neural networks (RNNs) usually face issues such as short-term memory and vanishing gradient problems [46,47]. In addition, this technique is not appropriate for processing long-term sequencing because it ignores the significant information from the earlier input level [48].…”

Section: Bilstm For Data Decodingmentioning

confidence: 99%

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

et al. 2021

Self Cite

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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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Towards Efficient Building Designing: Heating and Cooling Load Prediction via Multi-Output Model

Cited by 38 publications

References 40 publications

Short-Term Energy Forecasting Framework Using an Ensemble Deep Learning Approach

Short-Term Energy Forecasting Framework Using an Ensemble Deep Learning Approach

An Attention-Based Multilayer GRU Model for Multistep-Ahead Short-Term Load Forecasting

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

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