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- W4280490070 abstract "Load Forecasts are the primary factors which considered by electricity utility companies while planning power generation, power infrastructural development and load flows etc. Different forecasting techniques have been proposed from statistical to artificial intelligence-based models and the area of research is still growing. In our research work, considering the real time data of 33KV bus system which is having 34 buses and 54 lines. In this case, forecast the day ahead scheduling of various parameters such as load real power (Pload), voltage magnitude at each bus, apparent power flow between buses and total transmission losses for hourly basis and also forecasted the mentioned parameters for 5 days. The actual real time values are compared with forecasted values using two existing methods namely Extreme Learning Machine (ELM), moving average and proposed Moving Average–Extreme Learning Machine (MA-ELM) algorithm. In addition to this, forecasted the loads and losses for short term and long-term forecasting cases and verified through MATLAB programming." @default.
- W4280490070 created "2022-05-22" @default.
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- W4280490070 creator A5073829492 @default.
- W4280490070 date "2022-05-12" @default.
- W4280490070 modified "2023-09-27" @default.
- W4280490070 title "Real-time Forecasting of Electrical Power System Loads using Moving Average-Extreme Learning Machine (MA-ELM) Algorithm" @default.
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- W4280490070 doi "https://doi.org/10.37394/23203.2022.17.26" @default.
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