Forecasting of future electricity demand is very important for the electric power industry. As influenced by various factors, it has been shown in several publications that machine learning methods are useful for electric load forecasting (ELF). On the one hand, we introduce in this paper the approach of support vector regression (SVR) for ELF. In particular, we use particle swarm optimization ...

Renewable energy resources are being used to overcome energy shortage. From the point of view of energy management the interconnectivity of electric utility with renewable energy resources is difficult. Renewable energy resources need to be managed with electric utility. A Smart Energy Management System (SEMS) is designed and developed for monitoring an efficient load management of electric uti...

Magneto-electro-elastic materials are multiphase smart materials that exhibit coupling among electrical, magnetic and mechanical energy fields. Due to this ability, they have been the topic of numerous research in the past decade. In this paper, buckling behavior of a multiphase magneto-electro-elastic rectangular plate with simply supported boundary conditions is investigated, based on Reddy’s...

Electric Vehicles (EVs) require energy storage system that can address its fast changing load requirements such as sudden acceleration or quick regenerative braking by being able to supply or absorb high power as demanded. Lead Acid Batteries, commonly used in cheaper EVs (like electric tempo in Nepal), can properly supply these variable loads and get recharged but this mode of operation drasti...

Electric load forecasting is a key element for management and operation of the electric grid. In this study we introduce ensemble re-forecast methods that take an initial forecast and produce a better prediction by extracting information from the structured errors. The models in the ensemble rely upon the real-time information obtained from load measurements and estimates over a state-wide doma...

Electric distribution system planning is to provide an economic expansion plan to meet the future demands in its territory. A forecast of the future electric demand and its geographic distribution is a prerequisite for distribution planning. The quality and accuracy of this forecast have large influence on the quality of the electrical distribution system planning. Spatial load forecasting emer...

Energy delivery at well construction in regions with hard climate is distinguished by variable demand in electric and heat power. Energy consumption of drilling equipment, production and living facilities varies greatly during the year, depending on power load charts and climate conditions. Power supply of remote well construction sites is mostly accomplished by operating autonomous power sourc...

The paper examines the cointegration of the daily electric power system load and the weather by a field intelligent system. The daily load has been modelled by dynamic regressions. A "Daily Artificial Dispatcher" thermal intelligent system has been constructed. Time and energy tests have been obtained for this intelligent system. The improvement in the daily load forecast, achieved by this inte...

The large fluctuations in charging loads of electric vehicles (EVs) make short-term forecasting challenging. In order to improve the load performance EV load, a corresponding model-based multi-channel convolutional neural network and temporal (MCCNN-TCN) are proposed. (MCCNN) can extract fluctuation characteristics at various time scales, while (TCN) build time-series dependence between forecas...