This paper presents a recently developed simulation software tool for ac predictive and mitigation techniques for pipeline networks influenced by high-voltage (HV) power lines. The software can deal with any configuration (no limitation on number of pipes, transmission lines, bonds, groundings, coating, and soil resistivity) and is very user friendly and robust since a general applicable algorithm is used to calculate the induced electromagnetic force (EMF), eliminating the need for a subdivision of the pipelines in sections parallel or not to the transmission line(s). With the calculated values for the EMF, the induced voltages and currents are then obtained by solving the well-known transmission-line model using a numerical technique that allows to specify the pipeline parameters (diameter, coating, soil resistivity, ...) for each individual section of the pipeline. In this paper, simulation results will be presented and compared with available theoretical test cases. It will be demonstrated that the calculated values for the induced EMF and the induced voltage and current are in perfect agreement with these theoretical test cases. In addition, it will be proven that commonly used formulas for the induced EMF need to be handled with care, especially when the distance between the transmission line and the pipeline becomes bigger.

In the current trend of consumption, electricity consumption will become a very high cost for the end-users. Consumers acquire energy from suppliers who use short, medium, and long-term forecasts to place bids in the power market. This study offers a detailed analysis of relevant literature and proposes a deep learning methodology for forecasting industrial electric usage for the next 24 h. The hourly load curves forecasted are from a large furniture factory. The hourly data for one year is split into training (80%) and testing (20%). The algorithms use the previous two weeks of hourly consumption and exogenous variables as input in the deep neural networks. The best results prove that deep recurrent neural networks can retain long-term dependencies in high volatility time series. Gated recurrent units (GRU) obtained the lowest mean absolute percentage error of $4.82\%$ for the testing period. The GRU improves the forecast by $6.23\%$ compared to the second-best algorithm implemented, a combination of GRU and Long short-term memory (LSTM). From a practical perspective, deep learning methods can automate the forecasting processes and optimize the operation of power systems.

The inductance calculation and the layout optimization for spiral inductors still are research topics of actuality and very interesting especially in radio frequency integrated circuits. Our research work is fixed on the vast topics of this research area. In this effect we create a software program dedicate to dc inductance calculation and to layout optimization for spiral inductors. We use a wide range of inductors in the application made with our program; we compare our applications results with measurements results existing in the literature and with three-dimensional commercial field solver results in order to validate it. Our program is accurate enough, has a very friendly interface, is very easy to use and its running time is very sort compared with other similar programs. Since spiral inductors tolerance is generally on the order of several percent, a more accurate program is not needed in practice. The program is very useful for the spiral inductor design because it calculates the inductance of spiral inductors with a very good accuracy and also for the spiral inductor optimization, because it optimize the spiral inductor layouts in terms of technological restrictions and/or in terms of the designers' needs.

Integration of industrial consumers into the smart grid concept can be facilitated by optimizing load forecasting for industrial consumers. Minimizing forecast errors can improve the supplier-consumer relationship by reducing balancing costs and anticipate possible network faults. The present paper aims to research the efficiency of machine learning applied for industrial load. The dataset consists of hourly recorded values for electricity consumption generated by a meat processing facility. In the context of installing complex monitoring systems with high frequency recording intervals, huge amounts of data will be generated that require detalied analysis and real time processing, otherwise the investments in the smart grid are not justified, consequently disfavouring the development and digitization of electrical networks. Integration of the industrial consumer into the smart grid concept can be applied in great detail at large industrial consumers through robust forecasting. Forecasting the energy behaviour of a industrial consumer is a difficult task, high forecasting errors have been obtained due to the unpredictability of the consumer.