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Electromagnetic (EM) fields at a point in space generated by electric/magnetic dipole embedded in a general anisotropic stratified media can be analytically solved [1] by decomposing the fields into transverse electric (TE) and transverse magnetic(TM) modes. The electromagnetic field strengths are dependent on the nature (electric or magnetic) of dipoles, their placement in the stratified media, and relative location of dipoles. A three-layer stratified media has been employed in this paper to model the environment consisting of air, seawater, seabed. The mathematical formulations provided by Tang [1] are used for EM field calculation containing both magnitude and phase information ofẼ andH fields for dipole antenna of 4 types namely: horizontal magnetic dipole along x− direction (HMDx), horizontal electric dipole along x− direction (HEDx), vertical magnetic dipole (VMD) and vertical electric dipole (VED). Parametric computational investigations are presented to delineate effects of i) the electric and magnetic properties of the media, ii) the nature and orientations of dipole sources, on EM fields at a specified observation point. It is observed that contributions from horizontal electric dipoles have comparable electric and magnetic field strengths at an observation point in the air.

Most of the water utilities in the U.S. consume a lot of electrical energy for water treatment and delivery. Despite being large energy consumers, priority is not given to electric load forecasting in water utilities. An accurate forecast of electric load can pave the way to shaving peak demand and reducing high electricity bills. This paper applies a popular statistical approach named Auto Regressive Integrated Moving Average (ARIMA) and Deep Learning techniques to forecast daily electric load over a period of a month and 15-minute moving average electric load of a day for two sites in a southern California water utility. A comparative performance of these techniques with relevant error metrics has been introduced. The electric load of a water treatment plant and a pumping station have been forecasted with these two methods. Deep Learning techniques result in better load prediction for both accounts and in both time resolutions. This allows operators to take possible appropriate actions resulting in reduced electrical demand for any given billing period.