Intermittency in the grid creates operational issues for power system operators (PSO). One such intermittent parameter is load. Accurate prediction of load key to proper planning system. This paper uses regression analyses short-term forecasting (STLF). Assumed data are first analyzed and outliers identified treated. The cleaned fed methods involving Linear Regression, Decision Trees (DT), Supp...

Short-term load forecasting is crucial for the operations planning of an electrical grid. Forecasting the next 24 h of electrical load in a grid allows operators to plan and optimize their resources. The purpose of this study is to develop a more accurate short-term load forecasting method utilizing non-linear autoregressive artificial neural networks (ANN) with exogenous multi-variable input (...

With the rapid growth of power system and the increase in their complexity of the networks, load forecasting plays a vital role in economic operation of power systems, network planning and infrastructure development. Electricity demand forecasting is concerned with the prediction of a very short term, short term, medium term and long term load demand, depending on the time horizon. This paper p...

The scheduling and operation of power system becomes prominently complex and uncertain, especially with the penetration of distributed power. Load forecasting matters to the effective operation of power system. This paper proposes a novel deep learning framework to forecast the short-term grid load. First, the load data is processed by Box-Cox transformation, and two parameters (electricity pri...

This paper describes three software applications for distribution network load forecasting in a Smart Grid environment: (i) short-term feeder load forecasting, (ii) shortterm substation transformer load forecasting and transformer rating, and (iii) next-year load pocket forecasting. The shortterm feeder load forecasting allows a utility to reduce the possibility of feeder overloading. The subst...

This paper aims to develop an evolutionary deep learning based hybrid data driven approach for short term load forecasting (STLF) in the context of Bangladesh. With lapse time, power system is getting complex. Penetration intermittent renewable energy (RE) into grid, changing prosumer pattern with need demand side management (DSM) has thrown a challenge dynamic operation and control. Load plays...

Short term load forecasting is always an important study from operational and planning point of view. But short term price forecasting is a new topic. In this study, with the implementation of machine learning techniques, a new algorithm is proposed to predict both load and price values. A machine learning techniques such as Principle Component Analysis, and K nearest neighbor points, are appli...