Short Term Load Forecasting (STLF) is a power system operating procedures that have an important role in terms of realizing the economic electric production. This research focuses on the application of hybrid PSO-ANN algorithm in STLF. Load data grouped by the type of weekdays and holidays. Consumption of electricity load in West Java Indonesia, used as input to the learning algorithm PSO-ANN. ...

Load forecasting is a central integral process in the planning and operation of electric utilities. Load forecasting has become in recent years one of the major areas of research in electrical engineering. The main problem for the planning is the determination of load demand in the future. Because electrical energy cannot be stored appropriately, correct load forecasting is very essential for t...

Neural network technology has been successfully applied in many business areas. This paper systematic summarizes the applications of neural network technology in supply chain management, which include three areas: optimization , forecasting and decision support. Then, the BP neural network is used to forecast the demand of bicycle in a certain region, the forecasting result demonstrates that BP...

This report discusses methods for forecasting hourly loads of a US utility as part of the load forecasting track of the Global Energy Forecasting Competition 2012 hosted on Kaggle. The methods described (gradient boosting machines and Gaussian processes) are generic machine learning / regression algorithms and few domain specific adjustments were made. Despite this, the algorithms were able to ...

Under the situation that single prediction can’t meet the accuracy requirements, combined forecasting method is usually the first choice. Using the history data of domestic oil products consumption, by optimized combination of grey forecasting GM(1, 1) model and artificial neural network, the combined forecasting model was built. It can be getting from the combined forecasting model that on the...

Using a twenty-four (24) month period of single-dimensional sales data reported in a recent published article, our analysis shows that 'Marketers should not solely rely on a commercial forecasting package to make their forecasting estimation. Indeed, small and mid-size business managers can achieve significant savings by wisely utilizing their educated business experience along with a spreadshe...

This paper proposes a new method for crude oil price forecasting based on support vector machine (SVM). The procedure of developing a support vector machine model for time series forecasting involves data sampling, sample preprocessing, training & learning and out-of-sample forecasting. To evaluate the forecasting ability of SVM, we compare its performance with those of ARIMA and BPNN. The expe...

Load forecasting plays a significant role in power systems and smart buildings in efficient planning, distribution and management of power. Various exogenous and meteorological factors, gave made accurate load forecasting complex making it a challenging task. In recent years, the research on shortterm power load forecasting has become inevitable for the reliable and efficient functioning of pow...

Many forecasting models have been proposed to improve forecasting accuracy. Recently, Chen at al. model which incorporates three concepts of Fibonacci sequence, framework of Song and Chissom's model and weighted method of Yu's model has been proposed as a method to improve forecasting accuracy. However, the issue on lengths of intervals has not been investigated by Chen et al. despite Huarng ad...

Unscheduled maintenance of aircraft can cause significant costs. The machine needs to be repaired before it can operate again. Thus it is desirable to have concepts and methods to prevent unscheduled maintenance. This paper proposes a method for forecasting the condition of aircraftt air conditioning system based on observed past data. Forecasting is done in a point by point way, by iterating t...