A fog based load forecasting strategy based on multi-ensemble classification for smart grids

Abstract

Internet of things (IoT) improves the development and operation of smart electrical grids (SEGs). Overcoming the cloud challenges, 2-tier architecture is replaced by 3-tier one for including a fog computing tier that acts as a bridge between the IoT devices embedded in SEG and cloud. Load forecasting is an essential process for the electrical system operation and planning as it provides intelligence to energy management. This paper completes the electrical load forecasting (ELF) strategy introduced (Rabie et al. in Cluster Comput 22(1):241–270, 2019). ELF consists of two phases which are; (1) data pre-processing Phase (DP2) and (2) load prediction phase (LP2). Both phases can be performed in the cloud tier on the stored data which is sent from fogs to cloud at cloud servers. DP2 aims to perform feature selection and outlier rejection using data mining techniques. The main contribution of this paper focuses on LP2 providing multi-ensemble load prediction (MELP) method which can be learned by the filtered data from DP2 to give fast and accurate predictions. MELP can deal with big electrical data based on Map-Reduce method. It mainly consists of two levels which are; (1) local ensemble level (LEL) in map phase and (2) global ensemble level (GEL) in reduce phase. In LEL, the ensemble classification principle is applied at every device in map phase. In GEL, the perfect and final decision for load prediction is taken in reduce phase based on global judger (GJ) method from many local predictions which are the results of all devices in map phase. The conducted experimental results have shown that the proposed MELP outperforms recent prediction methods in terms of accuracy, precision, recall, F1-measure, and run time. It is concluded that the proposed MELP method can deal with big electrical data. It has a good impact in maximizing system reliability, resilience, and stability as it introduces fast and accurate load predictions.