As an important part of power system planning and the basis of economic operation of power systems, the main work of power load forecasting is to predict the time distribution and spatial distribution of future power loads. The accuracy of load forecasting will directly influence the reliability of the power system. In this paper, a novel short-term Empirical Mode Decomposition-Grey Relational ...

This study proposes using a random forest model for short-term electricity load forecasting. This is an ensemble learning method that generates many regression trees (CART) and aggregates their results. The model operates on patterns of the time series seasonal cycles which simplifies the forecasting problem especially when a time series exhibits nonstationarity, heteroscedasticity, trend and m...

 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...

Gas demand possesses dual property of growing and seasonal fluctuation simultaneously, it makes gas demand variation possess complex nonlinear character. From previous studies know single model for nonlinear problem can’t get good results but accurately gas forecast were essential part of an efficient gas system planning and operation. In recent years, lots of scholar put forward combination mo...

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...

Establishing an accurate and robust short-term load forecasting (STLF) model for a power system in safe operation rational dispatching is both required beneficial. Although deep long memory (LSTM) networks have been widely used applications, it still has some problems to optimize, such as unstable network performance optimization time. This study proposes adaptive step size self-organizing migr...

• A novel distributed DDBN model for short term load forecasting is proposed. The proposed can be trained under a framework, which doesn’t need central controller. Markovian-based switching topology designed to deal with uncertain cyberattacks during neighbour communication. has high potential handling massive and data. In modern power systems, centralised (STLF) methods raise concern on commun...

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...

Net energy metering tariffs have encouraged the growth of solar PV in the distribution grid. The additional variability associated with weather-dependent renewable energy creates new challenges for power system operators that must maintain and operate ancillary services to balance the grid. To deal with these issues power operators mostly rely on demand load forecasts. Electric load forecast ha...