No abstract

This paper presents an intelligent system for the detection of non-technical losses of electrical energy associated with the fraudulent behaviors of system users. This proposal has three stages: a non-supervised clustering of consumption profiles based on a hybrid algorithm between self-organizing maps (SOM) and genetic algorithms (GA). A second stage for demand forecasting is based on ARIMA (autoregressive integrated moving average) models corrected intelligently through neural networks (ANN). The final stage is a classifier based on random forests for fraudulent user detection. The proposed intelligent approach was trained and tested with real data from the Colombian Caribbean region, where the utility reports energy losses of around 18% of the total energy purchased by the company during the five last years. The results show an average overall performance of 82.9% in the detection process of fraudulent users, significantly increasing the effectiveness compared to the approaches (68%) previously applied by the utility in the region.

The electrical sector needs to study how energy demand changes to plan the maintenance and purchase of energy assets properly. Prediction studies for energy demand require a high level of reliability since a deviation in the forecasting demand could affect operation costs. This paper proposed a short-term forecasting energy demand methodology based on hierarchical clustering using Dynamic Time Warp as a similarity measure integrated with Artificial Neural Networks. Clustering was used to build the typical curve for each type of day, while Artificial Neural Networks handled the weather sensibility to correct a preliminary forecasting curve obtained in the clustering stage. A statistical analysis was carried out to identify those significant factors in the prediction model of energy demand. The performance of this proposed model was measured through the Mean Absolute Percentage Error (MAPE). The experimental results show that the three-stage methodology was able to improve the MAPE, reaching values as good as 2%.

No abstract

In this paper, an architecture based on computational intelligence for time series modeling is proposed to guarantee the automatic adjustability of trained models no matter the dynamic behavior of the modeled phenomena. The proposed method can assess the performance; and then proposes a maintenance routine for the time-series model. Thus, an auditor is devised to identify when a model must be updated before losing forecast performance. It has been determined that the MAPE (Mean Absolute Percentage Error) metric could not reveal changes in the model predicted curve contributing to invalidating the model, in particular if a non-stationary behavior is expected in the studied phenomena. Therefore, the novel rMAPE performance metric is proposed, so that the auditor does detect that the updating process does not achieve better performance; the system opts for replacing the time-series modeling techniques included in an available knowledge base. The intelligent system allows building time-series models automatically considering exogenous variables such as weather, calendar, and statistical transformations that can lead to the number of models required for a particular application. The proposed approach has been experimentally tested for power consumption and energy price via simulation. The forecasting results showed an improvement in the MAPE of up to 23% in the tests performed.INDEX TERMS. Forecasting, intelligent systems, time series modeling.