Fuzzy Load forecasting plays a paramount role in the operation and management of power systems. Accurate estimation of future power demands for various lead times facilitates the task of generating power reliably and economically. The forecasting of future loads for a relatively large lead time (months to few years) is studied here (long term load forecasting). Among the various techniques used...

We describe our approach to the Western Power Distribution (WPD) Presumed Open Data (POD) 6 MWh battery storage capacity forecasting competition, in which we finished second. The competition entails two distinct aims maximise daily evening peak reduction and using as much solar photovoltaic energy possible. For latter, combine a Bayesian (MCMC) linear regression model with an average generation...

As evidenced by an extensive empirical literature, multiplicative error models (MEM) show good performance in capturing the stylized facts of nonnegative time series; examples include, trading volume, financial durations, and volatility. This paper develops a bootstrap based method for producing multi-step-ahead probability forecasts valued time-series obeying parametric MEM. In order to test a...

BACKGROUND Over the past few decades, numerous forecasting methods have been proposed in the field of epidemic forecasting. Such methods can be classified into different categories such as deterministic vs. probabilistic, comparative methods vs. generative methods, and so on. In some of the more popular comparative methods, researchers compare observed epidemiological data from the early stages...

We propose novel smart forecasting models for Direct Normal Irradiance (DNI) that combine sky image processing with Artificial Neural Network (ANN) optimization schemes. The forecasting models, which were developed for over 6 months of intra-minute imaging and irradiance measurements, are used to predict 1 min average DNI for specific time horizons of 5 and 10 min. We discuss optimal models for...

As one of the most promising kinds of the renewable energy power, wind power has developed rapidly in recent years. However, wind power has the characteristics of intermittency and volatility, so its penetration into electric power systems brings challenges for their safe and stable operation, therefore making accurate wind power forecasting increasingly important, which is also a challenging t...

river flow forecasting for a region has a special and important role for optimal allocation of water resources. in this research, for forecasting river flow process, fuzzy inference system (fis) is used. three parameters including precipitation, temperature and daily discharge are used for forecasting of daily river flow of lighvan river located in lighvanchai watershed. for the initial preproc...

On the basis of the AR(1) stochastic process model for consumer demand which was introduced by Professor H.L.Lee, qualified and simulation model of bullwhip effect are established when order-up-to inventory policy is employed, which investigate demand variability caused by forecasting technology, such as moving average (MA) method, exponentially weighted moving average (EWMA) method or mean squ...