Analysis of the impact of regional temperature pattern on the energy consumption in the commercial sector in Japan

Hirano, Yoshiyuki; Gomi, Kei; Nakamura, Shōgo; Yoshida, Yukiko; Narumi, Daisuke; Fujita, Tsuyoshi

doi:10.1016/j.enbuild.2017.05.054

Cited by 13 publications

(4 citation statements)

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“…Therefore, we are developing a method of estimating energy consumption by heating and cooling from monthly energy consumption and temperature variation pattern. We have already proposed a method in the commercial sector to extract the variation components due to temperature from seasonal change information [52]. It is considered that our method is also applicable in the residential sector by using monthly data from the Family Income and Expenditure Survey.…”

Section: Discussionmentioning

confidence: 99%

Estimation of Direct and Indirect Household CO2 Emissions in 49 Japanese Cities with Consideration of Regional Conditions

et al. 2020

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We conducted a detailed estimation of direct and indirect CO2 emissions related to multi-person households in 49 Japanese cities. Direct energy consumption was decomposed into energy use in order to consider the relationship with regional conditions. The results showed that CO2 emissions from direct energy consumption were almost as large as indirect CO2 emissions induced by consuming products and services, suggesting that lifestyle improvements are important for both energy savings and reducing CO2 emissions relating to product and service consumption. In addition, CO2 emissions from direct energy consumption varied widely between cities, making them susceptible to regional conditions. We also calculated CO2 emissions from direct energy consumption and examined the regional conditions for individual forms of energy use. CO2 emissions were higher in cold regions and lower in larger cities. In Japan, large cities are often located in relatively warm areas, so we conducted an analysis to distinguish the effects of climatic conditions from those of urbanization. This analysis allowed us to clarify the effects of regional conditions on factors such as heating/cooling and the ratio of detached houses to apartments.

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Section: Discussionmentioning

confidence: 99%

Estimation of Direct and Indirect Household CO2 Emissions in 49 Japanese Cities with Consideration of Regional Conditions

et al. 2020

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“…The increasing use of air conditioning and heating due to being easily influenced by the air temperature. Therefore, studying the air temperature fluctuations is necessary to estimate the energy requirements needed [5]. Everyone can find information about future temperature conditions by doing temperature forecasts.…”

Section: Introductionmentioning

confidence: 99%

Time Series Temperature Forecasting by using ConvLSTM Approach, Case Study in Jakarta

Rasyid

Adytia

2022

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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Climate change has occurred in several countries, especially in tropical countries such as Indonesia. It causes extreme temperature changes in several Indonesian areas, especially Jakarta, one of the world's most populated cities. The population of Jakarta causes the activities carried out by residents to be disturbed by extreme temperature changes. In addition, drastic temperature changes also affect the energy consumption used by residents. Therefore, it is necessary to predict temperature to determine future temperature conditions so that residents can plan their activities. Temperature forecast can be done in several ways, one of which uses a machine learning approach. This research uses a deep learning model called the Convolutional Long Short-Term Memory (ConvLSTM). Moreover, we also compare the model with Multi-Layer Perceptron (MLP), and Long Short-Term Memory (LSTM). We use temperature data taken from the ERA-5 period years 2018 to 2020 located in Kemayoran, Jakarta, Indonesia. This research aims to investigate the accuracy of short-term temperature forecasting by using these three models. The model is built to predict short-term temperatures for 1, 3, and 7 days ahead. The performance of the three methods is measured by calculating the Root Mean Square Error (RMSE), Mean Square Error (MAE), and Coefficient Correlation (CC). The result shows that the LSTM performs better than the other methods to forecast 1, 3, and 7 days, i.e., with the lowest RMSE, MAE, and higher CC.

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“…As shown by recent examples, such as locally implemented plans to counteract global warming, formulation of new regional energy conservation visions, and proposals for environmental model/future cities, there are many opportunities for local governments to employ energy-efficient measures and CO 2 emission reduction plans [6]. Energy consumption for cooling and heating is strongly influenced by local factors such as climate, land use, and building-related aspects [7,8]. The potential for energy and CO 2 reductions from both nonstructural and structural measures is high [9], and there are proposals for such measures that incorporate various regional efforts.…”

Section: Introductionmentioning

confidence: 99%

Introduction of Low-Carbon Community Energy Systems by Combining Information Networks and Cogeneration-Type District Heating and Cooling Systems

Hirano¹,

Nakamura²,

Gomi³

et al. 2018

Sustainable Air Conditioning Systems

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Achievement of a low-carbon society is becoming extremely important. In this report, we introduce an example of carbon dioxide (CO 2) emission reductions and energy savings, using a local energy-control system. Our research is focused on the town of Shinchi in the Fukushima Prefecture, Japan. Shinchi is pursuing initiatives to create a low-carbon, energy-efficient society and a disaster-resilient community. The National Institute for Environmental Studies provides academic support for the design and planning of lowcarbon community energy systems by the local government of Shinchi, based on the Basic Cooperation Agreement. For the redevelopment of the Japan Railway (JR) Shinchi Station district that is being carried out, construction of a cogeneration-type district heating and cooling system is currently in progress. CO 2 reductions of about 20% can be expected by introducing this community energy system. To support these initiatives, we have developed an information and communication technology (ICT) system that shares a wide range of local information to support energy conservation. By analyzing electricity consumption data from the ICT, we evaluated the pattern of residential power consumption and confirmed that the project supports energy-saving behavior within the community. Additionally, the community energy project in the JR Shinchi Station district enables adjustment of the supply and demand balance.

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Analysis of the impact of regional temperature pattern on the energy consumption in the commercial sector in Japan

Cited by 13 publications

References 13 publications

Estimation of Direct and Indirect Household CO2 Emissions in 49 Japanese Cities with Consideration of Regional Conditions

Estimation of Direct and Indirect Household CO2 Emissions in 49 Japanese Cities with Consideration of Regional Conditions

Time Series Temperature Forecasting by using ConvLSTM Approach, Case Study in Jakarta

Introduction of Low-Carbon Community Energy Systems by Combining Information Networks and Cogeneration-Type District Heating and Cooling Systems

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