Employing electricity-consumption monitoring systems and integrative time-series analysis models: A case study in Bogor, Indonesia

Maki, Seiya; Ashina, Shuichi; Fujii, Minoru; Fujita, Tsuyoshi; Yabe, Norio; Uchida, Kenji; Ginting, Gito; Boer, Rizaldi; Chandran, Remi

doi:10.1007/s11708-018-0560-4

Cited by 13 publications

(7 citation statements)

References 11 publications

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“…In 2020, residential and commercial buildings accounted for approximately 22% and 18%, respectively, of the total U.S. end-use energy according to statistics from the U.S. Energy Information Administration (EIA). Therefore, the reduction in end-use energy consumption by buildings is crucial to meet the goal of energy conservation [3]. Accurately predicting the energy use in buildings is important for energy planning and energy savings.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED: Multiscale convolutional recurrent neural network for residential building electricity consumption prediction

Wang

et al. 2022

IFS

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The prediction of residential building electricity consumption can help provide an early warning regarding abnormal energy use and optimize energy supply. In this study, a multiscale convolutional recurrent neural network (MCRNN) is proposed to predict residential building electricity consumption. The MCRNN model uses multiscale convolutional units to collect different information on environmental factors, such as temperature, air pressure, light, and uses a bidirectional recurrent neural network (Bi-RNN) to extract the long-term dependence information of these factors. In addition, a recurrent convolutional connection is used to filter the most useful multiscale and long-term information in the MCRNN model. The accuracy of MCRNN is evaluated through an experiment using real data. The results show that MCRNN performs better than the other models. For instance, compared with the support vector regression (SVR) and random forest (RF) models, the MCRNN model has a 47.83% and 38.72% lower root mean square error (RMSE), respectively. The MCRNN model also shows a 37.81% and 70.38% higher accuracy, respectively, compared to the SVR and RF models.

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Section: Introductionmentioning

confidence: 99%

RETRACTED: Multiscale convolutional recurrent neural network for residential building electricity consumption prediction

Wang

et al. 2022

IFS

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“…Methods such as support vector machine [16], decision trees [16], [46], Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) [37], ridge regression [37], partition trees [49], CART [51], random forest [49], [51], and linear regression [24], [49], [50] were used. Neural network was also commonly used, as in [16], [38], [1], [48], [43], [9], [49], and [50]. The methods were performed on the whole sample or on a sample divided into clusters, as in [38] and [22].…”

Section: Previous Researchmentioning

confidence: 99%

The independent component analysis with the linear regression – predicting the energy costs of the public sector buildings in Croatia

Mokriš

2022

Croat. oper. res. rev. (Online)

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In the European Union, the public sector buildings are considered significant energy consumers and are, thus, the subject of several directives that aim to ensure the renovation of existing and the construction of new buildings as nearly zero-energy buildings. Therefore, as part of the decision making, it is necessary to properly plan the renovation or construction. This research provides models for predicting the energy costs of the public sector buildings, which are dependent upon its characteristics (i. e., constructional, occupational, energy, etc.). For this purpose, a real data set of Croatian public buildings was used, which included 150 variables and 1724 observations. Since the data set consisted of a large number of variables, the motivation for the dimensionality reduction was addressed first. Then, the independent component analysis, the principal component analysis, and the factor analysis were performed as the dimensionality reduction methods for variable extraction. The results of these analyses were used as inputs for modelling the energy costs of the public sector buildings. The obtained models were compared to the model built on original variables. The obtained models show the application potential in decision making for building renovation and construction in the public sector of Croatia, whereas the best performance of prediction in terms of RMSE and SMAPE was achieved by the model that integrated the independent component analysis with the linear regression.

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“…A yield-learning process describes the increase in yield due to various learning activities [40,41]. For example,  As time goes by, operators become increasingly skilled, which can help to avoid misoperation.…”

Section: Uncertain Yield-learning Processmentioning

confidence: 99%

“…A yield-learning process describes the increase in yield due to various learning activities [40,41]. For example,…”

Section: Uncertain Yield-learning Processmentioning

confidence: 99%

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Energy-Efficient Production Planning Using a Two-Stage Fuzzy Approach

Tsai

Chen

et al. 2021

Mathematics

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Analyzing energy consumption is an important task for a factory. In order to accomplish this task, most studies fit the relationship between energy consumption and product design features, process characteristics, or equipment types. However, the energy-saving effects of product yield learning are rarely considered. To bridge this gap, this study proposes a two-stage fuzzy approach to estimate the energy savings brought about by yield improvement. In the two-stage fuzzy approach, a fuzzy polynomial programming approach is first utilized to fit the yield-learning process of a product. Then, the relationship between monthly electricity consumption and increase in yield was fit to estimate the energy savings brought about by the improvement in yield. The actual case of a dynamic random-access memory factory was used to illustrate the applicability of the two-stage fuzzy approach. According to the experiment results, product yield learning can greatly reduce electricity consumption.

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Employing electricity-consumption monitoring systems and integrative time-series analysis models: A case study in Bogor, Indonesia

Cited by 13 publications

References 11 publications

RETRACTED: Multiscale convolutional recurrent neural network for residential building electricity consumption prediction

RETRACTED: Multiscale convolutional recurrent neural network for residential building electricity consumption prediction

The independent component analysis with the linear regression – predicting the energy costs of the public sector buildings in Croatia

Energy-Efficient Production Planning Using a Two-Stage Fuzzy Approach

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