Gaining Insights into Dwelling Characteristics Using Machine Learning for Policy Making on Nearly Zero-Energy Buildings with the Use of Smart Meter and Weather Data

Machine learning models have proven to be reliable methods in classification tasks. However, little research has been conducted on the classification of dwelling characteristics based on smart meter and weather data before. Gaining insights into dwelling characteristics, which comprise of the type of heating system used, the number of inhabitants, and the number of solar panels installed, can be helpful in creating or improving the policies to create new dwellings at nearly zero-energy standard. This paper compares different supervised machine learning algorithms, namely Logistic Regression, Support Vector Machine, K-Nearest Neighbor, and Long-short term memory, and methods used to correctly implement these algorithms. These methods include data pre-processing, model validation, and evaluation. Smart meter data, which was used to train several machine learning algorithms, was provided by Groene Mient. The models that were generated by the algorithms were compared on their performance. The results showed that the Long-short term memory performed the best with 96% accuracy. Cross Validation was used to validate the models, where 80% of the data was used for training purposes and 20% was used for testing purposes. Evaluation metrics were used to produce classification reports, which indicates that the Long-short term memory outperforms the compared models on the evaluation metrics for this specific problem

Gaining Insights into Dwelling Characteristics Using Machine Learning for Policy Making on Nearly Zero-Energy Buildings with the Use of Smart Meter and Weather Data.

Machine learning models have proven to be reliable methods in classification tasks. However, little research has been conducted on the classification of dwelling characteristics based on smart meter and weather data before. Gaining insights into dwelling characteristics, which comprise of the type of heating system used, the number of inhabitants, and the number of solar panels installed, can be helpful in creating or improving the policies to create new dwellings at nearly zero-energy standard. This paper compares different supervised machine learning algorithms, namely Logistic Regression, Support Vector Machine, K-Nearest Neighbor, and Long-short term memory, and methods used to correctly implement these algorithms. These methods include data pre-processing, model validation, and evaluation. Smart meter data, which was used to train several machine learning algorithms, was provided by Groene Mient. The models that were generated by the algorithms were compared on their performance. The results showed that the Long-short term memory performed the best with 96% accuracy. Cross Validation was used to validate the models, where 80% of the data was used for training purposes and 20% was used for testing purposes. Evaluation metrics were used to produce classification reports, which indicates that the Long-short term memory outperforms the compared models on the evaluation metrics for this specific problem.post-print1304 K

Wave damping potential of woody riparian vegetation: Comparing terrestrial laser scanning with manual measuring techniques

Including vegetation in flood defense systems has the potential to be a more cost-effective solution than conventional dike reinforcement measures, as vegetation contains wave damping properties. However, more insight is required on the complex physical processes due to wave-vegetation interaction in order to predict the amount of wave damping. Past research found that these processes are dependent on both flow conditions and vegetation characteristics. Therefore, reliable quantification of these vegetation characteristics isof importance to understand these processes and thus to improve predictions for wave dissipation due to vegetation. This study aims to gain insight on practical methods for quantifying relevant vegetation characteristicsfor wave damping. Data is used from full scale physical experiments conducted in the Delta Flume at Deltares, with 40 meters of willow forest. The vegetation characteristics are quantified in this study both by manual measurements on the willow trees and by executing Terrestrial 3D laser scans (TLS) of the whole forest. The frontal area of vegetation is found in literature to be a relevant parameter for determining the wave attenuation, therefore the focus in this study lies on schematizing this parameter over the vertical, which is seen as representations of the average willow tree. This schematization is referred to in this report as “tree model”. In this study, four tree models are obtained. The four tree models include: tree model 1a (manual measurements on the primary branches, excluding side branches), tree model 1b (branching method based on Strahlers ordering scheme, including side branches), tree model 1c (adjusted branching method) and tree model 2 (from terrestrial laser scanner). The reconstructed area from the TLS point cloud is determined by using Matlab built-in alpha shape function. The tree models are compared in terms of frontal area distribution over the vertical and of their effect on the corresponding wave attenuation. The latter comparison is achieved by using the numerical wave model SWAN and confronting the results with the measured wave attenuation from the physical experiments. With regards to the manual measuring methods, the frontal area of the average tree from tree model 1a serves as a lower limit, while tree model 1b gives the upper limit. The TLS outcome (tree model 2) underestimates the frontal area as computed by the other tree models. In particular, the underestimation is 70% when compared with tree model 1b, and 30 % when compared to tree model 1a. Adjustments on tree model 1b leads to tree model 1c. This tree model accounts for the tapering form of the branches and results in a total frontal area in between tree model 1a and tree model 1b. In this study, the representative area for the willow trees can be best captured with tree model 1c. This tree model results in a drag coefficient (CD) of 1.15 averaged over the tests with leafless willows and showed a negative correlation with the Keulegan-Carpenter number (KC). However, the capabilities of the TLS should be analyzed further and this study encourages to use a larger data set of trees in order to find a relation between the laser penetration and corresponding mismatch. Gathering of vegetation parameters by hand is in fact not economically attractive forwoody riparian vegetation, as these trees are characterized by complex canopy structures and high elevations. The TLS can serve as a practical tool for obtaining these relevant vegetation parameters for applying willows in hybrid solutions.WOODY projectCivil Engineering | Hydraulic Engineerin

Development of control strategy of DC-DC converter for optimal operation of PV powered Electrolyser

With the current need of implementing renewable energy sources to combat climate change, significant developments are being done into storage options for these intermittent energy sources. Green Hydrogen can be produced by the connection of a renewable energy source to an electrolyser which produces hydrogen, in this project the Photovoltaic (PV) System is indirectly connected to the electrolyser as this gives the ability to control the different systems and flexibility in sizing the different systems. Several different dc/dc converters are simulated in Matlab Simulink and compared to each other while also taking into account the project requirements. First, an overview of the project is presented after which the main idea of the thesis is presented in which the dc/dc converters are selected for optimal operation of the system. Two converters are selected based on simulations and mathematical calculations (Component sizes, Voltage ripple & Current ripple), for the Maximum Power Tracking converter (Connected to the PV system) the buck-boost converter was selected. This was because of its ability to fully track the Current-Voltage curve of a PV system. And for the connection to the Electrolyser system a 3-level Interleaved Buck converter was selected, because of its increased reliability to be able to continue working after a power electronic switch failure. With the PV system maximum power point being between 580-582 V (depending on the irradiance) and a Electrolyser voltage range between 210 - 260 V, the voltage needed to be reduced. After selecting the different converters, a control system was modelled and simulated, this is to optimize and control the working points of the PV-Electrolyser system. This control system works on the idea of matching the electrolyser load working point to the available PV power from the input. This control algorithm selects the right reference voltage which then goes into a voltage controller to ensure the correct voltage is at the output for the electrolyser load. This control algorithm was modelled and simulated in Matlab simulink and tested against different changing inputs (Changing the irradiance & electrolyser temperature). Furthermore, the controller was checked for several different stepsizes and time delays (accounting for external effects) and the optimal combination was found to be a stepsize of 0.1V and timedelay of 1ms which gave an Converter+algorithm efficiency of 98.42%. The results demonstrate the controller’s ability to correctly follow the irradiance pattern and electrolyser temperature changes.Electrical Engineering | Electrical Power Engineerin

Impact of high penetration of solar PV generation on transient- and voltage stability of the Dutch high voltage grid

The last decade has seen an immense growth in solar PV systems (both large-scale solar PV systems and distributed solar PV systems). It is expected that this growth will continue due to environmental concerns. Since conventional generators are defined by their physics and control, and solar PV systems are defined solely by their control algorithms, this transition has brought with it challenges for preservation of power system stability. Presently, solar PV systems are not modelled in detail in the current dynamic grid model of TenneT. However, due to the flourishing of solar PV systems and lessening of synchronous generators, the role of solar PV systems on the dynamic behaviour of power systems keeps increasing hence an increased need for detailed modelling is necessary. In this thesis report, solar PV systems i.e. large-scale solar PV systems and aggregation of distributed solar PV systems, are modelled according to the requirements of the Dutch gridcode (netcode) and the European 'Requirements for Generators'. Additionally, a standard modelling approach, including proper selection of models and a default parameter set, for representation of large-scale and aggregated PV systems is developed. Following this, the analysis of transient- and voltage stability is firstly conducted on an IEEE 9 bus network with different PV penetration levels. In addition, several case studies are selected based on TenneT's investment plan scenario to determine the impact of a high penetration of PV systems on transient- and voltage stability in a larger, more extensive network.Electrical Engineerin

Improving the quality control of Cofra Roller Compaction: A study on the relation between the impact acceleration and the soil compaction

Ground improvement in the form of soil compaction plays a important part in reclamation projects. The development of the Cofra Roller Compaction (CRC), a non-circular impact roller, has proven to be valuable in these projects. However, the heterogeneity of the subsoil causes locally a non-uniform degree of compaction. Traditional compaction control tests are limited in measuring depth, expensive and cause time delay. Therefore, the Continuous Compaction Control (CCC) and Continuous Impact Response (CIR)method were developedin order to provide more real-time information of the compaction based on the response of the drum of the roller. The aim of this study is to develop a semi-empirical energy model which is based on the contact forces of the roller-soil interface as most CCC systems, but also uses field test data as was given in the CIR system to validate this model. The relevant parameters needed for this model were obtained from the field test conducted for the HES Hartel Tank Terminal project in Rotterdam. These included the impact acceleration, the cone resistance, the in situ density, the dynamic modulus and the dynamic plate load test velocity. Two methods are considered in this thesis and both aim to reproduce the measured values from the dynamic plate load test during the field test. The first method considers the acceleration signals and includes double numerical integration of these signals to obtain the displacement, while the other considers modelling the roller as a dynamic plate load test and obtaining the displacement from solving a 2-DOF spring-mass-damper system.However, after analysis of the motion of the roller, it was observed that due to the non-circular shape of the roller, a wedge effect was created where horizontal shearing forces caused loosening of the soil. This inhibited soil compaction up to 0.5 m depth. The impact acceleration signals were thus not representative of the soil compaction. Nonetheless, the DPL-Soil model was proven to be successful in correlating the soil settlement to the dynamic modulus. This study considers a silty sand, so further research should be carried out to obtain correlations for various soils. In order to develop the semi-empirical energy model, it is thus recommended to capture an accurate acceleration response. This can be done by placing accelerometers at a minimum depth of 0.5 m, replacing the 8G accelerometer with e.g. 16G accelerometer and increasing the sampling rate to at least 1000 Hz. In order to filter out the soil variability, a field test with the roller should be performed on a homogeneous sand without fines. Correlations can then be drawn again for the same field tests performed in this thesis. Finite Element Modelling (FEM) could be used to model the interaction between the non-circular shape of the lobe, the rolling motion and the soil. This might form a better correction method for the acceleration signals than those explained in this thesis. Low frequency geophones can be used to measure the velocity directly. This because low frequency data of the accelerometer should be removed and the roller works on a low frequency. The load imparted to the ground could also be measured directly by burying earth pressure cells at a minimum depth of 0.5m and at various depths to get a more accurate representation of the pressure distribution through the soil layers. By using other numerical integration methods such as Simpson’s 3/8 rule and Boole’s rule, the numerical accuracy of the displacement response of the roller could also be improved.Applied Earth Science