Feature selection for probabilistic load forecasting via sparse penalized quantile regression

Probabilistic load forecasting(PLF)is able to present the uncertainty information of the future loads.It is the basis of stochastic power system planning and operation.Recent works on PLF mainly focus on how to develop and combine forecasting models,while the feature selection issue has not been thoroughly investigated for PLF.This paper fills the gap by proposing a feature selection method for PLF via sparse L1-norm penalized quantile regression.It can be viewed as an extension from point forecasting-based feature selection to probabilistic forecasting-based feature selection.Since both the number of training samples and the number of features to be selected are very large,the feature selection process is casted as a large-scale convex optimization problem.The alternating direction method of multipliers is applied to solve the problem in an efficient manner.We conduct case studies on the open datasets of ten areas.Numerical results show that the proposed feature selection method can improve the performance of the probabilistic forecasting and outperforms traditional least absolute shrinkage and selection operator method.

Embedding based quantile regression neural network for probabilistic load forecasting

Compared to traditional point load forecasting,probabilistic load forecasting(PLF) has great significance in advanced system scheduling and planning with higher reliability. Medium term probabilistic load forecasting with a resolution to an hour has turned out to be practical especially in medium term energy trading and can enhance the performance of forecasting compared to those only utilizing daily information. Two main uncertainties exist when PLF is implemented: the first is the temperature fluctuation at the same time of each year; the second is the load variation which means that even if observed indicators are fixed since other observed external indicators can be responsible for the variation. Therefore, we propose a hybrid model considering both temperature uncertainty and load variation to generate medium term probabilistic forecasting with hourly resolution. An innovative quantile regression neural network with parameter embedding is established to capture the load variation, and a temperature scenario based technique is utilized to generate temperatureforecasting in a probabilistic manner. It turns out that the proposed method overrides commonly used benchmark models in the case study.

Active-reactive scheduling of active distribution system considering interactive load and battery storage

Distributed generation(DG)are critical components for active distribution system(ADS).However,this may be a serious impact on power system due to their volatility.To this problem,interactive load and battery storage may be a best solution.This paper firstly investigates operation characteristics of interactive load and battery storage,including operation flexibility,inter-temporal operation relations and active-reactive power relations.Then,a multi-period coordinated activereactive scheduling model considering interactive load and battery storage is proposed in order to minimize overall operation costs over a specific duration of time.The model takes into accounts operation characteristics of interactive load and battery storage and focuses on coordination between DGs and them.Finally,validity and effectiveness of the proposed model are demonstrated based on case study of a medium-voltage 135-bus distribution system.