The Number of Nodes Effect to Predict the Electrical Consumption in Seven Distinct Countries

This paper presents a machine learning-based approach for forecasting electrical consumption in seven selected countries across different geographical categories. The data, sourced from The International Energy Agency, is analysed and condensed to focus on specific nations: Northern (Norway, Canada), Southern (Chile, Australia), Four-season (France, Japan), and a Tropical country (Colombia). The unique electrical consumption patterns influenced by regional climate characteristics make this study compelling for machine learning applications. From the dataset comprising over 132,000 records from January 2010 to May 2023 across 53 countries, a refined dataset focusing on 791 data points from seven specifically chosen countries to simplify the study. A significant part of the paper details the machine learning design for electrical consumption forecasting. Specifically, Artificial neural network architecture is proposed to predict consumption. The input features encompass the year, month, and country, with the output being the anticipated electrical usage

Lessons learnt from mining meter data of residential consumers

Tracking end-users' usage patterns can enable more accurate demand forecasting and the automation of demand response execution. Accordingly, more advanced applications, such as electricity market design, integration of distributed generation and theft detection can be developed. By employing data mining techniques on smart meter recordings, the suppliers can efficiently investigate the load patterns of consumers. This paper presents applications where data mining of energy usage can derive useful information. Higher demands, on one side, and the energy price increase on the other side, have caused serious issues with regards to electricity theft, especially among developing countries. This phenomenon leads to considerable operational losses within the electrical network. In order to identify illegal residential consumers, a new method of analysing and identifying electricity consumption patterns of consumers is proposed in this paper. Moreover, the importance of data mining for analysing the consumer's usage curves was investigated. This helps to determine the behaviour of end-users for demand response purposes and improve the reliability and security of the electricity network. Clustering load profiles for large scale energy datasets are discussed in detail

Assessing the effects of power quality on partial discharge behaviour through machine learning

Partial discharge (PD) is commonly used as an indicator of insulation health in high voltage equipment, but research has indicated that power quality, particularly harmonics, can strongly influence the discharge behaviour and the corresponding pattern observed. Unacknowledged variation in harmonics of the excitation voltage waveform can influence the insulation's degradation, leading to possible misinterpretation of diagnostic data and erroneous estimates of the insulation's ageing state, thus resulting in inappropriate asset management decisions. This paper reports on a suite of classifiers for identifying pertinent harmonic attributes from PD data, and presents results of techniques for improving their accuracy. Aspects of PD field monitoring are used to design a practical system for on-line monitoring of voltage harmonics. This system yields a report on the harmonics experienced during the monitoring period

An Analytical Approach to Network Motif Detection in Samples of Networks with Pairwise Different Vertex Labels

Network motifs, overrepresented small local connection patterns, are assumed to act as functional meaningful building blocks of a network and, therefore, received considerable attention for being useful for understanding design principles and functioning of networks. We present an extension of the original approach to network motif detection in single, directed networks without vertex labeling to the case of a sample of directed networks with pairwise different vertex labels. A characteristic feature of this approach to network motif detection is that subnetwork counts are derived from the whole sample and the statistical tests are adjusted accordingly to assign significance to the counts. The associated computations are efficient since no simulations of random networks are involved. The motifs obtained by this approach also comprise the vertex labeling and its associated information and are characteristic of the sample. Finally, we apply this approach to describe the intricate topology of a sample of vertex-labeled networks which originate from a previous EEG study, where the processing of painful intracutaneous electrical stimuli and directed interactions within the neuromatrix of pain in patients with major depression and healthy controls was investigated. We demonstrate that the presented approach yields characteristic patterns of directed interactions while preserving their important topological information and omitting less relevant interactions

Numerical simulation of density-driven flow and heat transport processes in porous media using the network method

Density-driven flow and heat transport processes in 2-D porous media scenarios are governed by coupled, non-linear, partial differential equations that normally have to be solved numerically. In the present work, a model based on the network method simulation is designed and applied to simulate these processes, providing steady state patterns that demonstrate its computational power and reliability. The design is relatively simple and needs very few rules. Two applications in which heat is transported by natural convection in confined and saturated media are studied: slender boxes heated from below (a kind of Bénard problem) and partially heated horizontal plates in rectangular domains (the Elder problem). The streamfunction and temperature patterns show that the results are coherent with those of other authors: steady state patterns and heat transfer depend both on the Rayleigh number and on the characteristic Darcy velocity derived from the values of the hydrological, thermal and geometrical parameters of the problems.The first author acknowledges the support of the Universidad Politécnica de Cartagena through a pre-doctoral scholarship and the economic support of the Universidad Católica del Norte to cover the costs to publish in open access

Characterisation of anisotropic etching in KOH using network etch rate function model: influence of an applied potential in terms of microscopic properties

Using the network etch rate function model, the anisotropic etch rate of p-type single crystal silicon was characterised in terms of microscopic properties including step velocity, step and terrace roughening. The anisotropic etch rate data needed have been obtained using a combination of 2 wagon wheel patterns on different substrate and 1 offset trench pattern. Using this procedure the influence of an applied potential has been investigated in terms of microscopic properties. Model parameter trends show a good correlation with chemical/electrochemical reaction mechanism and mono- and dihydride terminated steps reactivity difference. Results also indicate a minimum in (111) terrace roughening which results in a peak in anisotropic ratio at the non-OCP applied potential of −1250 mV vs OCP