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The shift towards distributed generation and microgrids has renewed the interest in forecasting algorithms and methods, which need to take into account the advances in information, metering and control technologies in order to address the challenges of forecasting problems. Technologies such as machine learning have been proven useful for short-term electricity load forecasting, especially for microgrids, as they can also take into account several types of historical data and can adapt to changes often encountered in small-scale systems and on a short time scale. In this paper, we present a flexible and easily customized modular toolbox, called Divinus, for electricity use profiling and forecasting in microgrids. Divinus may support a variety of machine learning algorithms for forecasting and profiling that can be used independently or combined. For demonstration purposes, we have implemented Self-Organizing Maps for profiling and k-Neighbors for forecasting. The testing of the platform was based on electricity consumption data of the Euripus campus of the National and Kapodistrian University of Athens in Evia, Greece, from January 2010 till March 2018. The tests that have been carried out so far show that the platform can be easily customized and the algorithms examined yield high accuracy and acceptable mean errors for the case of a university campus energy profile.

The objective of this paper is to present a new fuzzy logic method for midterm energy forecasting. The proposed method properly transforms the input variables to differences or relative differences, in order to predict energy values not included in the training set and to use a minimal number of patterns. The input variables, the number of the triangular membership functions and their base widths are simultaneously selected by an optimization process. The standard deviation is calculated analytically by mathematical expressions based on the membership functions. Results from an extensive application of the method to the Greek power system and for different categories of customers are compared to those obtained from the application of standard regression methods and artificial neural networks (ANN). Index Terms--Energy forecasting, fuzzy logic, optimization of membership functions, standard deviation.

Existing magnetostrictive torque sensor designs typically measure the rotation of the saturation magnetization under an applied torque and their theoretical treatment revolves around the minimization of the free energy equation adapted according to the assumptions considered valid in each design. In the torque measurement design discussed in this paper, Ni-rich NiFe films have been electrodeposited on cylindrical austenitic steel rods. Contrary to existing designs, the excitation field is applied along the axial direction and is low enough to ensure that the resulting magnetization along the same direction remains in the linear region of the M(H) characteristic. Assuming homogeneous magnetization, positive magnetostriction constant λ, negligible hysteresis and demagnetizing fields, torque T may be expressed in terms of an effective uniaxial anisotropy constant Ku around 45° to the axial direction. It is shown, that for the proposed arrangement, the resulting M is the linear superposition of the effect of a torque-induced effective field and the excitation field, the applied field accounts for the vertical offset of the magnetization response and the applied torque increases the slope of the M(H) characteristic.

Smart grid is not a system, application, network, or single device. It is no surprise that there is no unique definition of the Smart Grid (SG): ABB says is an evolved grid system that manages electricity demand in a sustainable, reliable and economic manner, built on advanced infrastructure and tuned to facilitate the integration of all involved elements. However, the definitions from authoritative organizations follow a common theme: Smart grids utilize information and communication technologies to manage the energy distribution and optimize the transmission from suppliers to consumers and vice versa. Capacitor banks and reclosersneed the intelligent control to appropriately adapt the supply to the demand. In this paper, we discuss communication systems and the requirements they have to fulfill in the smart grid applications.