| 1. |
- Ding, Yiyu, et al.
(författare)
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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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Ingår i: Energy Conversion and Management. - : Elsevier BV. - 0196-8904 .- 1879-2227. ; 269
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Tidskriftsartikel (refereegranskat)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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| 2. |
- Du, Rong, 1989-, et al.
(författare)
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Comparing Backscatter Communication and Energy Harvesting in Massive IoT Networks
- 2022
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Ingår i: IEEE Transactions on Wireless Communications. - : Institute of Electrical and Electronics Engineers (IEEE). - 1536-1276 .- 1558-2248. ; 21:1, s. 429-443
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Tidskriftsartikel (refereegranskat)abstract
- Backscatter communication (BC) and radio-frequency energy harvesting (RF-EH) are two promising technologies for extending the battery lifetime of wireless devices. Although there have been some qualitative comparisons between these two technologies, quantitative comparisons are still lacking, especially for massive IoT networks. In this paper, we address this gap in the research literature, and perform a quantitative comparison between BC and RF-EH in massive IoT networks with multiple primary users and multiple low-power devices acting as secondary users. An essential feature of our model is that it includes the interferences caused by the secondary users to the primary users, and we show that these interferences significantly impact the system performance of massive IoT networks. For the RF-EH model, the power requirements of digital-to-analog and signal amplification are taken into account. We pose and solve a power minimization problem for BC, and we show analytically when BC is better than RF-EH. The results of the numerical simulations illustrate the significant benefits of using BC in terms of saving power and supporting massive IoT, compared to using RF-EH. The results also show that the backscatter coefficients of the BC devices must be individually tunable, in order to guarantee good performance of BC.
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| 3. |
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| 5. |
- Figueras, Jordi-Lluis, et al.
(författare)
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Sharp 1/2-Hölder continuity of the Lyapunov exponent at the bottom of the spectrum for a class of Schrödinger cocycles
- 2020
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Ingår i: Discrete and Continuous Dynamical Systems. - : American Institute of Mathematical Sciences. - 1078-0947 .- 1553-5231. ; 40:7, s. 4519-4531
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Tidskriftsartikel (refereegranskat)abstract
- We consider the setting for the disappearance of uniform hyperbolicity as in Bjerklov and Saprykina (2008 Nonlinearity 21), where it was proved that the minimum distance between invariant stable and unstable bundles has a linear power law dependence on parameters. In this scenario we prove that the Lyapunov exponent is sharp 1/2-Holder continuous. In particular, we show that the Lyapunov exponent of Schrodinger cocycles with a potential having a unique non-degenerate minimum is sharp 1/2-Holder continuous below the lowest energy of the spectrum, in the large coupling regime.
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| 6. |
- Habib, Mustapha, PhD, et al.
(författare)
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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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Ingår i: Sustainable cities and society. - : Elsevier BV. - 2210-6707. ; 90
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Tidskriftsartikel (refereegranskat)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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| 7. |
- Hasselberg, Adam, et al.
(författare)
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Cliffhanger : An Experimental Evaluation of Stateful Serverless at the Edge
- 2024
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Ingår i: 2024 19th Wireless On-Demand Network Systems and Services Conference. - : IEEE. ; , s. 41-48
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Konferensbidrag (refereegranskat)abstract
- The serverless computing paradigm has transformed cloud service deployment by enabling automatic scaling of resources in response to varying demand. Building on this, stateful serverless computing introduces critical capabilities for data management, fault tolerance, and consistency, which are particularly relevant in the context of distributed deployments, notably in edge computing environments. In this work, we explore the feasibility of stateful serverless computing in resource-limited edge environments through an empirical study utilizing a multi-view object tracking application. Our results show that while these systems perform well in cloud environments, their effectiveness is severely affected at the edge due to state, application, and resource management solutions optimized for cloud environments. Existing solutions are most detrimental to applications with intermittent workloads, as typical combinations of concurrency handling and resource reservation can lead to minutes of unstable system behavior due to cold starts. Our results highlight the need for a tailored approach in stateful serverless systems for edge computing scenarios.
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| 8. |
- Ohlson Timoudas, Thomas, et al.
(författare)
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A General Framework to Distribute Iterative Algorithms with Localized Information over Networks
- 2023
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Ingår i: IEEE Transactions on Automatic Control. - : Institute of Electrical and Electronics Engineers Inc.. - 0018-9286 .- 1558-2523. ; 68:12, s. 7358-
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Tidskriftsartikel (refereegranskat)abstract
- Emerging applications in IoT (Internet of Things) and edge computing/learning have sparked massive renewed interest in developing distributed versions of existing (centralized) iterative algorithms often used for optimization or machine learning purposes. While existing work in the literature exhibit similarities, for the tasks of both algorithm design and theoretical analysis, there is still no unified method or framework for accomplishing these tasks. This paper develops such a general framework, for distributing the execution of (centralized) iterative algorithms over networks in which the required information or data is partitioned between the nodes in the network. This paper furthermore shows that the distributed iterative algorithm, which results from the proposed framework, retains the convergence properties (rate) of the original (centralized) iterative algorithm. In addition, this paper applies the proposed general framework to several interesting example applications, obtaining results comparable to the state of the art for each such example, while greatly simplifying and generalizing their convergence analysis. These example applications reveal new results for distributed proximal versions of gradient descent, the heavy-ball method, and Newton's method. For example, these results show that the dependence on the condition number for the convergence rate of this distributed Heavy ball method is at least as good as for centralized gradient descent. Author
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| 9. |
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| 10. |
- Ohlson Timoudas, Thomas, et al.
(författare)
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Enabling Massive IoT in Ambient Backscatter Communication Systems
- 2020
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Ingår i: ICC 2020 - 2020 IEEE International Conference on Communications (ICC). - : IEEE.
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Konferensbidrag (refereegranskat)abstract
- Backscatter communication is a promising solution for enabling information transmission between ultra-low-power devices, but its potential is not fully understood. One major problem is dealing with the interference between the backscatter devices, which is usually not taken into account, or simply treated as noise in the cases where there are a limited number of backscatter devices in the network. In order to better understand this problem in the context of massive IoT (Internet of Things), we consider a network with a base station having one antenna, serving one primary user, and multiple IoT devices, called secondary users. We formulate an optimization problem with the goal of minimizing the needed transmit power for the base station, while the ratio of backscattered signal, called backscatter coefficient, is optimized for each of the IoT devices. Such an optimization problem is non-convex and thus finding an optimal solution in real-time is challenging. In this paper, we prove necessary and sufficient conditions for the existence of an optimal solution, and show that it is unique. Furthermore, we develop an efficient solution algorithm, only requiring solving a linear system of equations with as many unknowns as the number of secondary users. The simulation results show a lower energy outage probability by up to 40-80 percentage points in dense networks with up to 150 secondary users. To our knowledge, this is the first work that studies backscatter communication in the context of massive IoT, also taking into account the interference between devices.
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