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.

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.

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.

This paper proposes a privacy-preserving algorithm to solve the average-consensus problem based on Shamir’s secret sharing scheme, in which a network of agents reach an agreement on their states without exposing their individual states until an agreement is reached. Unlike other methods, the proposed algorithm renders the network resistant to the collusion of any given number of neighbors (even with all neighbors’ colluding). Another virtue of this work is that such a method can protect the network consensus procedure from eavesdropping.

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.

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.

In this thesis we study the process of torus collisions in one-parameter families of quasi-periodically forced dynamical systems. Specifically, we study the process whereby two invariant curves (homeomorphic to circles), one attracting and one repelling, bifurcate into a strange non-chaotic attractor. In Paper A, the system is a quasi-periodically forced logistic family, but avoids period-doubling. We give an asymptotic analysis of some geometric properties of the attractor, as it approaches the repeller at the bifurcation point. In Paper B, we study the same type of questions as in Paper A, but instead for a class of quasi-periodic C^2 Schrödinger cocycles. In both papers the results confirm a conjecture that the distance between the curves is asymptotically linear in the parameter, for those classes of systems. In addition, we obtain results about the asymptotic growth of C^1-norms. In Paper C, we study the same class of systems as in Paper B, but instead look at the asymptotics of the (maximal) Lyapunov exponent at the bifurcation point. The results show that it has Hölder exponent exactly 1/2, as the energy parameter approaches the lowest energy of the spectrum. This confirms, for this class and setting, similar conjectures about this asymptotic behaviour.

I denna avhandling studeras toruskollisioner i dynamiska system på skevproduktsform, som beror på en parameter. Mer specifikt studeras processen varvid två invarianta kurvor (homeomorfa med cirkeln), varav en attraherande och en repellerande, närmar sig varandra och resulterar i en attraktor med fraktal geometri. I Artikel A studeras en kvasiperiodiskt störd kvadratisk familj, som i detta fall inte genomgår någon perioddubblering. Resultaten visar asymptotiska lagar för hur geometrin hos de invarianta kurvorna ändras när de närmar sig bifurkationsparametern. I Artikel B studeras samma typ av frågor som i Artikel A, för en klass av kvasi-periodiskt störda Schrödinger-cocycler. I båda artiklarna visar resultaten att avståndet mellan kurvorna är asymptotiskt linjärt, med avseende på parametern, nära bifurkationen. Dessutom fås resultat för hur C^1-normerna växer nära bifurkationen. I Artikel C studeras samma klass av system som i Artikel B, men frågeställningen utgår i stället från det asymptotiska beteendet hos Lyapunovexponenten. Resultaten visar att Lyapunovexponenten har Hölderexponent exakt 1/2 för energier lägre än den lägsta energin i spektrumet. Detta visar, för denna klass, att Lyapunovexponenten uppför sig man förmodat.

Let Phi be a quasi-periodically forced quadratic map, where the rotation constant omega is a Diophantine irrational. A strange non-chaotic attractor (SNA) is an invariant (under Phi) attracting graph of a nowhere continuous measurable function psi from the circle T to [0, 1]. This paper investigates how a smooth attractor degenerates into a strange one, as a parameter beta approaches a critical value beta(0), and the asymptotics behind the bifurcation of the attractor from smooth to strange. In our model, the cause of the strange attractor is a so-called torus collision, whereby an attractor collides with a repeller. Our results show that the asymptotic minimum distance between the two colliding invariant curves decreases linearly in the parameter beta, as beta approaches the critical parameter value beta(0) from below. Furthermore, we have been able to show that the asymptotic growth of the supremum of the derivative of the attracting graph is asymptotically bounded from both sides by a constant times the reciprocal of the square root of the minimum distance above.