Publication

Abstract : Power supply regulation and load forecast are important factors in electric power distribution systems. The advent and ever-expanding adoption of renewables and distributed energy resources in the energy sector have introduced a lot of complexity into the day-to-day operations and maintenance of a wide-area power grid. Implementation of big data analysis and deep learning tools in power distribution systems has enabled predictive maintenance, grid health monitoring, demand forecasting, and reliability analysis, and also provided a host of other features for overall improvement of grid operations. A thorough analysis reflecting presents and future patterns can aid in critical decisions regarding generation capacity, transmission, and distribution systems for a successful load flow system planning. The focus of this paper is on ways to estimate load using the deep learning technique Long short-term memory (implemented by Python programming language).