The nonlinear theories of load forecasting, such as the applications of neural network and chaos, have recently made considerable progress. Generally, it is an effective method to combine phase space restructures theory with artificial neural networks (ANN) model for load forecasting. But, they are not so effective to forecast attractors with higher embedded dimension. The paper proposes a new ...

 Abstract—This article presents the review of the computing models applied for solving problems of midterm load forecasting. The load forecasting results can be used in electricity generation such as energy reservation and maintenance scheduling. Principle, strategy and results of short term, midterm, and long term load forecasting using statistic methods and artificial intelligence technology...

Load forecasting is important for safe and cost-effective operation of recent power utilities. It helps in taking many decisions regarding energy purchasing and generation, maintenance, etc. Further, load forecasting provides information which is able to be used for energy interchange with other utilities. Over the years, a number of methods have been proposed for load forecasting. This paper f...

As renewable energy increasingly integrates into the electric power system, electric load forecasting and renewable energy power generation forecasting become more important. In this project, ARIMA and NARX are applied to build load forecasting model focusing on improving statistical and computational efficiency without losing accuracy. ARIMA turns out to be better for short term forecasting wh...

Load Forecasting plays a key role in making today's and future's Smart Energy Grids sustainable and reliable. Accurate power consumption prediction allows utilities to organize in advance their resources or to execute Demand Response strategies more effectively, which enables several features such as higher sustainability, better quality of service, and affordable electricity tariffs. It is eas...

The expertise of electricity load forecasting has developed over decades. Some of the best load forecasting models use this expertise to improve the load forecasting accuracy by splitting the forecasting problem into sub-problems such as for weekend/weekday and peak/off peak. This research is designed to evaluate a method based on boosting algorithms to split the data into sub-problems for pric...

The short-term load forecasting is an important method for security dispatching and economical operation in electric power system, and its prediction accuracy directly affects the operating reliability of the electric system. So the global optimization ability of particle swarm optimization (PSO) algorithm and classification prediction ability of support vector machine (SVM) are combined in ord...

Leveraging smart metering solutions to support energy efficiency on the individual household level poses novel research challenges in monitoring usage and providing accurate load forecasting. Forecasting electricity usage is an especially important component that can provide intelligence to smart meters. In this paper, we propose an enhanced approach for load forecasting at the household level....

Load forecasting plays a critical role in Smart Grid. As there have been various types of customers with different behaviours in a Smart Grid, it would benefit load forecasting if customer behaviours were taken into consideration. This paper proposes a novel load forecasting method that efficiently explores customers’ power consumption behaviours through learning. Our method uses L1-CCRF to ini...

This paper presents a new combined method for the short-term load forecasting of electric power systems based on the Fuzzy c-means (FCM) clustering, particle swarm optimization (PSO) and support vector regression (SVR) techniques. The training samples used in this method are of the same data type as the learning samples in the forecasting process and selected by a fuzzy clustering technique acc...