This paper proposes a novel neural model to the problem of short-term load forecasting. The neural model is made up of two self-organizing map nets | one on top of the other. It has been successfully applied to domains in which the context information given by former events plays a primary role. The model was trained and assessed on load data extracted from a Brazilian electric utility. It was ...

This paper presents a new, accurate load forecasting technique robust to fluctuations due to unusual load behavioral changes in buildings, i.e., the potential for small commercial buildings with heterogeneous stores. The proposed scheme is featured with two functional components: data classification by daily characteristics and automatic forecast model switching. The scheme extracts daily chara...

Type-2 fuzzy logic to make up for the lack of a type of fuzzy logic in dealing with uncertainty, object contains uncertainty is strong; the application of type-2 fuzzy logic advantage is more obvious. In this paper, type-2s of fuzzy logic for power load time series forecasting, good results were obtained. According to the power load has strong randomness it is difficult to accurately forecast p...

Electricity load forecasting is a key task in the planning and operation of power systems and electricity markets, and its importance increases with the advent of smart grids. In this paper, we present AWNN, a new approach for very short-term load forecasting. AWNN decomposes the complex electricity load data into components with different frequencies that are predicted separately. It uses an a...