An efficient and accurate electrical power Short Term Load forecasting plays a vital role for economic operational planning of both the electricity markets as well as regulated power systems. Till date many techniques and approaches have been presented for STLF in the literature. However there is still an essential need to develop more efficient and accurate load forecast model. This paper uses...

The smart meter is an important part of the smart grid, and in order to take full advantage of smart meter data, this paper mines the electricity behaviors of smart meter users to improve the accuracy of load forecasting. First, the typical day loads of users are calculated separately according to different date types (ordinary workdays, day before holidays, holidays). Second, the similarity be...

Short-to-medium-term electric load forecasting is crucial for grid planning, transformation, and scheduling power supply departments. Various complex ever-changing factors such as weather, seasons, regional economic structures, enterprise production cycles exert uncontrollable effects on the load. While causal convolutional neural network can significantly enhance long-term sequence prediction,...

A novel approach to electric load forecasting using an hybrid method of artificial intelligence is presented. This paper proposes a combining of a numeric extrapolation of data with the methodology of case-based reasoning and expert system. The retrieve of previous cases adapted by an expert system has been overlapped to the load forecasting given by the numeric techniques, presently accepted f...