| 1. |
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- 2019
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Journal article (peer-reviewed)
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| 2. |
- Chen, Shuqin, et al.
(author)
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Usage strategy of phase change materials in plastic greenhouses, in hot summer and cold winter climate
- 2020
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In: Applied Energy. - : Elsevier BV. - 0306-2619 .- 1872-9118. ; 277
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Journal article (peer-reviewed)abstract
- Plastic greenhouses are basically used to create a warmed and protected growing area for plants. In the hot summer and cold winter climate, the consumption of the heating system for a greenhouse is the major operating cost. To reduce the production cost and limit the release of greenhouse gases, this investigation proposed the design of a latent heat storage system using phase change material for plastic greenhouses in this climate. Using a pilot in southern China, this study established a test bed of a greenhouse and developed a numerical model for designing the all-day use strategies in winter. The experimental data confirmed the feasibility of the strategy and validated the numerical model. Without using phase change material, the air temperature within the greenhouse could be as low as 3.7 degrees C; while the proposed strategy was able to maintain the indoor air temperature no less than 10 degrees C. The numerical model was further applied to design the all-day use strategies with different combinations of phase change material and insulation in a real greenhouse. The numerical simulations were able to help find the combination that satisfied the temperature requirement with the least investment. The payback time of the designed strategy was less than the lifespan.
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| 3. |
- Ding, Yiyu, et al.
(author)
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A study on data-driven hybrid heating load prediction methods in low-temperature district heating : An example for nursing homes in Nordic countries
- 2022
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In: Energy Conversion and Management. - : Elsevier BV. - 0196-8904 .- 1879-2227. ; 269
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Journal article (peer-reviewed)abstract
- In the face of green energy initiatives and progressively increasing shares of more energy-efficient buildings, there is a pressing need to transform district heating towards low-temperature district heating. The substantially lowered supply temperature of low-temperature district heating broadens the opportunities and challenges to integrate distributed renewable energy, which requires enhancement on intelligent heating load prediction. Meanwhile, to fulfill the temperature requirements for domestic hot water and space heating, separate energy conversion units on user-side, such as building-sized boosting heat pumps shall be implemented to upgrade the temperature level of the low-temperature district heating network. This study conducted hybrid heating load prediction methods with long-term and short-term prediction, and the main work consisted of four steps: (1) acquisition and processing of district heating data of 20 district heating supplied nursing homes in the Nordic climate (2016–2019); (2) long-term district heating load prediction through linear regression, energy signature curve in hourly resolution, providing an overall view and boundary conditions for the unit sizing; (3) short-term district heating load prediction through two Artificial Neural Network models, f72 and g120, with different prediction input parameters; (4) evaluation of the predicted load profiles based on the measured data. Although the three prediction models met the quality criteria, it was found that including the historical hourly heating loads as the input to the forecasting model enhanced the prediction quality, especially for the peak load and low-mild heating season. Furthermore, a possible application of the heating load profiles was proposed by integrating two building-sized heat pumps in low-temperature district heating, which may be a promising heat supply method in low-temperature district heating.
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| 4. |
- Habib, Mustapha, PhD, et al.
(author)
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A hybrid machine learning approach for the load prediction in the sustainable transition of district heating networks
- 2023
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In: Sustainable cities and society. - : Elsevier BV. - 2210-6707. ; 90
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Journal article (peer-reviewed)abstract
- Current district heating networks are undergoing a sustainable transition towards the 4th and 5th generation of district heating networks, characterized by the integration of different types of renewable energy sources (RES) and low operational temperatures, i.e., 55 ◦C or lower. Due to the lower temperature difference between supply and return, it is necessary to develop novel methods to understand the loads accurately and provide operation scenarios to anticipate demand peaks and increase flexibility in the energy network, both for long- and short- term horizons. In this study, a hybrid machine-learning (ML) method is developed, combining a clustering pre-processing step with a multi-input artificial neural network (ANN) model to predict heat loads in buildings cluster-wise. Specifically, the impact of time-series data clustering, as a pre-processing step, on the performance of ML models was investigated. It was found that data clustering contributes effectively to the reduction of data training costs by limiting the training processes to representative clusters only instead of all datasets. Additionally, low-quality data, including outliers and large measurement gaps, are excluded from the training to enhance the overall prediction performance of the models.
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| 5. |
- Luo, Ziteng, et al.
(author)
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Linking roots, preferential flow, and soil moisture redistribution in deciduous and coniferous forest soils
- 2022
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In: Journal of Soils and Sediments. - : Springer Science and Business Media LLC. - 1614-7480 .- 1439-0108.
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Journal article (peer-reviewed)abstract
- Soil moisture (i.e., the changes in the gravimetric soil water content) redistribution is closely linked with root distribution and preferential flow in soils. This study aimed at exploring the soil water content distribution in the presence of root-enhanced preferential flow in deciduous (Quercus variabilis BI.) and coniferous forests (Platycladus orientalis (L.)).
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