The role of hygrothermal modelling for different housing typologies by estimating indoor relative humidity, energy usage and anticipation of fuel poverty

Karyono, Kanisius; Romano, Abbie; Abdullah, Badr M.; Cullen, Jeff; Brás, Ana

doi:10.1016/j.buildenv.2021.108468

Cited by 6 publications

(2 citation statements)

References 32 publications

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“…However, fossil fuels have a major problem: they are limited in quantity and they release carbon dioxide into the atmosphere in the combustion process. The increase in carbon dioxide in the air is the main cause of global warming because carbon dioxide traps heat in the atmosphere [1]. To overcome these problems, renewable energy such as hydropower, geothermal, solar power, wind power, and biogas can be used instead of fossil fuels [2] [3].Since the 1970s, the use of solar energy as an alternative energy source to overcome the energy crisis has gained attention from many countries in the world.…”

Section: Introductionmentioning

confidence: 99%

Analysis Of Solar Power Plant Utilization For Public Street Lighting In Probolinggo, Jawa Timur, Indonesia

Saputri,

Richard Stanlee,

Hadi Prasetya

et al. 2023

International Journal of Science, Technology & Management

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Fossil fuels namely coal, petroleum, and natural gas have long been chosen as the main energy sources in meeting global energy needs. However, fossil fuels have the main problem in that their amount is limited and causes global warming. To overcome these problems, renewable energy such as solar energy can be used. Solar energy can be converted into electrical energy by using solar panels. The installation of a solar power plant certainly requires a large budget, to check its feasibility and can be simulated using PVSyst and RETScreen. In particular, PVSyst is used to search for the amount of material needed, the power capacity produced, and energy losses (there is energy lost). Then, RETScreen is used to calculate photovoltaic installation costs, operational and maintenance costs after photovoltaic installation, and emissions. Probolinggo has enormous potential to use solar power plants with Photovoltaic modules. This study simulates the installation of photovoltaic modules in Probolinggo which still has shortcomings in terms of public street lighting. From the simulations carried out, it can be concluded that Probolinggo is feasible for the installation of the solar power plant. With PVSyst, the total number of photovoltaic modules installed is 15,112 pieces. The total power generated is 10,145 MWh. Then, by using RETScreen, a photovoltaic installation fee of Rp142,978,410,000 was obtained. and annual maintenance costs of Rp1,281,870,000.

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Section: Introductionmentioning

confidence: 99%

Analysis Of Solar Power Plant Utilization For Public Street Lighting In Probolinggo, Jawa Timur, Indonesia

Saputri,

Richard Stanlee,

Hadi Prasetya

et al. 2023

International Journal of Science, Technology & Management

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“…As a result, the amount of heat transferred from the building walls was estimated to have increased by 1.5 to 1.9 times (Wang et al, 2022). In a study conducted by Karyono et al, it was discovered that estimating the relative humidity value and using this data in the building energy management system could save 2% of the energy consumed in buildings (Karyono et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Daily average relative humidity forecasting with LSTM neural network and ANFIS approaches

Özbek

Ünal

Bilgili

2022

Theor Appl Climatol

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Because hurricanes, droughts, oods, and heat waves are all important factors in measuring environmental changes, they can all result from changes in atmospheric air temperature and relative humidity (RH). Besides, climate, weather, industry, human health, and plant growth are all affected by RH.Accurately and consistently forecasting RH is a challenge due to its non-linear nature. The present study tried to predict one day ahead of RH in determined provinces from different climatic regions of Turkey (Ankara, Erzurum, Samsun, Diyarbakır, Antalya, and Bilecik) using long short-term memory (LSTM) and adaptive neuro-fuzzy inference system (ANFIS) with fuzzy c-means (FCM) based machine learning models. As evaluation criteria, root mean square error (RMSE), mean absolute error (MAE), and correlation coe cient (R) were employed. The outcomes from the forecasting models were also validated using observed data. During the testing stage, the smallest MAE and RMSE values were discovered to be 5.76% and 7.51%, respectively, in Erzurum province, with an R-value of 0.892 when using the LSTM method.Moreover, the smallest MAE and RMSE values were obtained to be 5.95% and 7.67% respectively, in Erzurum province with an R-value of 0.887 using the ANFIS model according to the hourly RH prediction.The results indicate that both the LSTM and ANFIS approaches performed well in daily RH prediction, with the LSTM and ANFIS approaches producing nearly identical results.

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Assessment of hygrothermal performance and mould growth risk in roofs of post-retrofitted non-dormer and dormer attic rooms with reduced ventilation

Liu,

Considine,

McNabola

2024

Building and Environment

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The role of hygrothermal modelling for different housing typologies by estimating indoor relative humidity, energy usage and anticipation of fuel poverty

Cited by 6 publications

References 32 publications

Analysis Of Solar Power Plant Utilization For Public Street Lighting In Probolinggo, Jawa Timur, Indonesia

Analysis Of Solar Power Plant Utilization For Public Street Lighting In Probolinggo, Jawa Timur, Indonesia

Daily average relative humidity forecasting with LSTM neural network and ANFIS approaches

Assessment of hygrothermal performance and mould growth risk in roofs of post-retrofitted non-dormer and dormer attic rooms with reduced ventilation

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