A Study on Recent Trends for Load Forecasting with Artificial Intelligence

Abstract

In order to manage and maintain the power supply in distribution grids. The decision makers in the power grids must predict/forecast the energy demand with the least possibility of error. With the appropriate load forecasting, a stable, continuous and cost-effective power can be supplied to the consumers. Various factors can affect the accuracy of the load forecasting such as load density, geographical factors, population growth etc. Load forecasting is divided into three types: long-term load forecasting, medium-term load forecasting and short-term load forecasting. This paper presents an overview for load forecasting and its types. Out of which, STLF plays a very significant role in ensuring that power systems works efficiently, safely and economically. Various STLF techniques were proposed by the researchers that are discussed in literature survey, in order to optimize the distribution in electrical power grids. However, STLF is complex method as its prediction accuracy gets altered by the various complicated and non-linear external parameters. To overcome the drawbacks of STLF, a large number of STLF, MTLF and LTLF methods such as MLR, KBES etc. were proposed. From the literature survey conducted, it is observed that if these methods are incorporated with the artificial intelligence systems along with various dependency factors then the efficiency of these systems can further be increased..