This paper presents an artificial neural network, ANN, based approach for estimating short-term wholesale electricity prices using past price and demand data. The objective is to utilize the piecewise continuous nature of electricity prices on the time domain by clustering the input data into time ranges where the variation trends are maintained. Due to the imprecise nature of cluster boundarie...

In this paper we make research in Residential short-term load forecasting. Different application scenes have different affecting factors of short-term load, so we should specifically analysis of factors that affect the load of the residential electricity. We use SPSS (Statistic Package for Social Science) to figure out the relationship between the daily load and temperature, weather conditions ...

In this paper, we study the efficiency of the Chinese wheat and soybeans futures markets and assess the conditions in agricultural commodity futures and cash markets in China. Formal statistical tests are conducted through Johansen's cointegration approach to identify the long-term equilibrium relationship between futures and cash markets. Three different cash prices from Zhengzhou Grain Wholes...

This paper presents the application of Fuzzy ARTMAP neural network for evaluating on-line load forecasting in short term case. A new approach using artificial neural networks (ANNs) is proposed for short term load forecasting. To forecast loads of a day, the hourly load pattern and the maximum and minimum and average of temprature must be determined. To demonstrate the effectiveness of the prop...

Prediction of peak loads in Iran up to year 2011 is discussed using the Radial Basis Function Networks (RBFNs). In this study, total system load forecast reflecting the current and future trends is carried out for global grid of Iran. Predictions were done for target years 2007 to 2011 respectively. Unlike short-term load forecasting, long-term load forecasting is mainly affected by economy...

Abstract. In this paper we propose a new forecasting methodology that comprises simultaneous level-wise modeling in the wavelet domain. The WAW methodology (short for wavelet-armax-winters) uses three modeling startegies: ARMAX models capable of incorporating external inputs and model feedbacks, trigonometric regressions sensitive to seasonality effects and Holt-Winters models describing trends...

the ability of artificial neural network (ann) for estimating the natural gas demand load for the next day and month of the populated cities has shown to be a real  concern. as the most applicable network, the ann with multi-layer back propagation perceptrons is used to approximate functions. throughout the current work, the daily effective temperature is determined, and then the weather data w...

Electricity prices strongly depend on seasonality of different time scales, therefore any forecasting electricity has to account for it. Neural networks have proven successful in short-term price-forecasting, but complicated architectures like LSTM are used integrate the seasonal behavior. This paper shows that simple neural network DNNs with an embedding layer information can generate a compet...