Many researchers around the world work on short term electricity demand forecasting (STLF) in order to establish an accurate power planning and generation system their countries. This research, with its focus short-term load forecasting, aims fill this gap by implementing two methodologies based Artificial Neural Network (ANN) Autoregressive Integrated Moving Average (ARIMA) applied a set of ha...

A modeling method is suggested in this paper that permits building fuzzy models for short-term load forecasting (STLF). The model building process is divided in three parts: (a) the structure identi6cation based on a fuzzy C-regression method, (b) selection of the proper model inputs which is achieved using a genetic algorithm based selection mechanism, and (c) 6ne tuning by means of a hybrid g...

Electric vehicle (EV) charging load is greatly affected by many traffic factors, such as road congestion. Accurate ultra short-term forecasting (STLF) results for regional EV are important to the scheduling plan of load, which can be derived realize optimal grid benefit. In this paper, a regional-level STLF method proposed and discussed. The usage degree all piles firstly defined us based on fr...

“Short-term load forecasting (STLF)” is increasingly significant because of the extensive use distributed energy resources, incorporation intermitted RES, and implementation DSM. This paper provides a novel ensemble model with wavelet transform for STLF depending on decomposition principle profiles. The can effectively capture portion daily profiles caused by seasonal variations. results indica...

With economic growth, the demand for power systems is increasingly large. Short-term load forecasting (STLF) becomes an indispensable factor to enhance application of a smart grid (SG). Other than aggregated residential loads in large scale, it still urgent problem improve accuracy individual energy users due high volatility and uncertainty. However, as important variable that affects consumpti...

Short-term load forecasting (STLF) has a significant role in reliable operation and efficient scheduling of power systems. However, it is still major challenge to accurately predict due social natural factors, such as temperature, humidity, holidays weekends, etc. Therefore, very important for the feature selection extraction input data improve accuracy STLF. In this paper, novel hybrid model b...

STLF is used in making decisions about economical power generation capacity, fuel purchasing, safety assessment, and power system planning in order to have economical power conditions. In this study, Turkey’s 24-hourahead load forecasting without meteorological data is studied. ANN, wavelet transform and ANN, wavelet transform and RBF NN, and EMD and RBF NN structures are used in STLF procedure...

Short-term load forecasting (STLF) plays a pivotal role in the electricity industry because it helps reduce, generate, and operate costs by balancing supply demand. Recently, challenge STLF has been variation that occurs each period, day, seasonality. This work proposes bagging ensemble combining two machine learning (ML) models—linear regression (LR) support vector (SVR). For comparative analy...

Short-term load forecasting (STLF) is essential for urban sustainable development. It can further contribute to the stable operation of smart grid. With development renewable energy, improving STLF accuracy has become a vital task. Nevertheless, most models based on convolutional neural network (CNN) cannot effectively extract crucial features from input data. The reason that fundamental requir...

Abstract Forecasting electrical energy demand and consumption is one of the important decision-making tools in distributing companies for making contracts scheduling and purchasing electrical energy. This paper studies load consumption modeling in Hamedan city province distribution network by applying ESN neural network. Weather forecasting data such as minimum day temperature, average day temp...