Electricity consumption characteristics in Thailand residential sector and its saving potential

Poolsawat, Kittikun; Tachajapong, Watcharapong; Prasitwattanaseree, Sukon; Wongsapai, Wongkot

doi:10.1016/j.egyr.2019.11.085

Cited by 24 publications

(10 citation statements)

References 2 publications

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“…Such small contribution in winter indicates that the UHI is not a major contributor to the large wintertime warming in Thailand, in line with the results of Ren et al [13] and Yang et al [25] for north and east China. The weaker wintertime UHI observed in Thailand may result from the combined effect of lower solar radiation and decreased anthropogenic heat release during winter season [64,65] thereby reducing urban to rural contrasts in energy balance [66]. On the other hand, the seasonal means of urban warming contribution generally increase from winter to spring and gradually decline thereafter (Table 2).…”

Section: Urban Warming Trends and Their Contribution To Overall Warmingmentioning

confidence: 99%

Urbanization effects on surface air temperature trends in Thailand during 1970-2019

Kachenchart

Kamlangkla

Puttanapong

et al. 2020

Environmental Engineering Research

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Continued urban expansion undergone in the last decades has converted many weather stations in Thailand into suburban and urban setting. Based on homogenized data during 1970-2019, therefore, this study examines urbanization effects on mean surface air temperature (Tmean) trends in Thailand. Analysis shows that urban-type stations register the strongest warming trends while rural-type stations exhibit the smallest trends. Across Thailand, annual urban-warming contribution exhibits a wide range (< 5% to 77%), probably manifesting the Urban Heat Island (UHI) differences from city to city resulting from the varied urban characteristics and climatic background. Country-wide average urban warming contribution shows a significant increasing trend of 0.15 <sup>o</sup>C per decade, accounting for 40.5% of the overall warming. This evidence indicates that urban expansion has great influence on surface warming, and the urban-warming bias contributes large fraction of rising temperature trends in Thailand. The increasing trend of annual Tmean for Thailand as a whole after adjusting urban-warming bias is brought down to the same rate as the annual global mean temperature trend, reflecting a national baseline signal driven by large-scale anthropogenic-induced climate change. Our results provide a scientific reference for policy makers and urban planners to mitigate substantial fraction of the UHI warming.

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Section: Urban Warming Trends and Their Contribution To Overall Warmingmentioning

confidence: 99%

Urbanization effects on surface air temperature trends in Thailand during 1970-2019

Kachenchart

Kamlangkla

Puttanapong

et al. 2020

Environmental Engineering Research

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“…In our development process we considered the weather conditions all year round. To ensure that the real-time power consumption data used for both augmentation and validation of the model's performance captures this fact, we juxtaposed the power generation capacity of test environment Thailand on the load factors based on urban and rural characterization discussed in [13] to test if climate changes have any effect on the characteristics of household electricity consumption. Load factors, seasonal factors, and utilizations factor are some of the usage characteristics relevant to the power consumption of air conditioners, fans, refrigerators, water heaters and even washing machines and clothes driers.…”

Section: The Network Architecturementioning

confidence: 99%

“…Location variation can invalidate the potency of a successful method when it is applied in another location with different weather and ambient characteristics. Therefore, a use case scenario [13] that characterizes power demand in urban areas, and rural areas across Thailand, was used for easy determination of likely energy demand in each category and to consider their various power consumption behavior. By this idea, the result of this predictions can therefore be compared with other predictions applicable to different locations and based on similar characterization .…”

Section: Datasetmentioning

confidence: 99%

Smart power consumption forecast model with optimized weighted average ensemble

Ndife

Rakwichian

Muneesawang

et al. 2022

IJ-AI

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Smart power forecasting enables energy conservation and resource planning. Power estimation through previous utility bills is being replaced with machine intelligence. In this paper, a neural network architecture for demand side power consumption forecasting, called SGtechNet, is proposed. The forecast model applies ConvLSTM-encoder-decoder algorithm designed to enhance the quality of spatial encodings in the input feature to make a 7-day forecast. A weighted average ensemble approach was used, where multiple models were trained but only allow each model’s contribution to the prediction to be weighted proportionally to their level of trust and estimated performance. This model is most suitable for low-powered devices with low processing and storage capabilities like smartphones, tablets and iPads. The power consumption comparison between a manually operated home and a smart home was investigated and the model’s performance was tested on a time-domain household power consumption dataset and further validated using a real time load profile collated from the School of Renewable Energy and Smart Grid Technology, Naresuan University Smart Office. An improved root mean square error (RMSE) of 358 kwh was achieved when validated with holdout validation data from the automated office. Overall performance error, forecast and computational time showed a significant improvement over published research efforts identified in a literature review.

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“…Los resultados del modelo revelan que la intervención regulatoria puede tener un impacto significativo en el consumo de electricidad y las emisiones de CO2 del sector residencial doméstico. Poolsawat, Tachajapong, et al (2020) Se presentó el estado actual de las características del consumo de electricidad en el sector residencial en Tailandia. Eran una información esencial para la estimación del uso total de energía.…”

Section: Antecedentesunclassified

Hábitos Sobre Consumo Responsable en Estudiantes De Una Universidad De La Región De Lambayeque

Muñoz

Huamán

Siesquen

et al. 2022

AIAPÆC

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La investigación tuvo como objetivo determinar el nivel de hábitos sobre consumo responsable en estudiantes de una universidad en la Región de Lambayeque. Para ello, se utilizó un diseño no experimental, empleando la encuesta como técnica y el instrumento cuestionario de preguntas. La metodología se basó en encuestar en forma aleatoria simple no estratificada de acuerdo con el interés de los investigadores en una sola oportunidad a 220 estudiantes que pertenecen al primer ciclo académico 2021-II de la universidad Señor de Sipán. Llegando a obtener resultados donde se evidencian el bajo nivel del consumo responsable por parte de los estudiantes universitarios participantes, donde se puede concluir el rol que juega la universidad para mejorar el nivel de hábitos de consumo en los estudiantes universitarios, donde sirvan de ejemplo a las demás personas de su entorno sobre buenos hábitos de consumo responsable.

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Electricity consumption characteristics in Thailand residential sector and its saving potential

Cited by 24 publications

References 2 publications

Urbanization effects on surface air temperature trends in Thailand during 1970-2019

Urbanization effects on surface air temperature trends in Thailand during 1970-2019

Smart power consumption forecast model with optimized weighted average ensemble

Hábitos Sobre Consumo Responsable en Estudiantes De Una Universidad De La Región De Lambayeque

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