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- W4289883989 abstract "Planners at utilities want automated and accurate load forecasts for short-, medium- and long-term load planning. Accurate forecasts are crucial to meet future load demand in response to national growth and to use as a baseline for tariff discussions. Here we propose an end-to-end approach for the data model, forecast model, and forecasting and demand reporting methods. Such an approach can help plan distribution across various geographic areas, such as urban areas, development areas, remote villages/villages, agricultural land, and farms. Such a solution and approach would also look at a top-down forecasting model to consider the electricity consumption pattern relative to macroeconomic factors and weather-related parameters and then disaggregate it while considering distribution losses and other parameters. This model also addresses the rebalancing of peak demand within a feeder. Optimization techniques can create a group of feeders for optimal demand management without needing a new feeder or substation. This study can be used for multiple purposes, and this system can provide a higher degree of predictability and productivity to identify gaps and accelerate reconciliation. Automated and peak load prediction solutions based on data and artificial intelligence can help create a variety of energy demand scenarios for annual peak load and energy requirements. Accurate load prediction can reduce the cost of holding capacity and reliably avoid power and related outages. Automating the forecasting process can help achieve consistent forecasting, supplement field knowledge, and reduce manual errors, leading to timely, high-level forecasts and efficient use of human resources." @default.
- W4289883989 created "2022-08-05" @default.
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- W4289883989 date "2022-01-01" @default.
- W4289883989 modified "2023-10-05" @default.
- W4289883989 title "Artificial intelligence-driven power demand estimation and short-, medium-, and long-term forecasting" @default.
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- W4289883989 doi "https://doi.org/10.1016/b978-0-323-90396-7.00013-4" @default.
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