An Accurate Dynamic Forecast of Photovoltaic Energy Generation

The accurate forecast of the photovoltaic generation(PVG)process is essential to develop optimum installation sizing and pragmatic energy planning and management.This paper proposes a PVG forecast model for a PVG/Battery installation.The forecasting strategy is built on a Medium-Term Energy Forecasting(MTEF)approach refined dynamically every hour(Dynamic Medium-Term Energy Forecasting(DMTEF))and adjusted by means of a Short-Term Energy Forecasting(STEF)strategy.The MTEF predicts the generated energy for a day ahead based on the PVG of the last 15 days.As for STEF,it is a combination between PVG Short-Term(ST)forecasting and DMTEF methods obtained by selecting the least inaccurate PVG estimation every 15 minutes.The algorithm results are validated by measures taken on a 3 KWp standalone PVG/Battery installation.The proposed approaches have been integrated into a management algorithm in order to make a pragmatic decision to ensure load supply considering relevant constraints and priorities and guarantee the battery safety.Simulation results show that STEF provides accurate results compared to measures in stable and perturbed days.The NMBE(Normalized Mean Bias Error)is equal to-0.58%in stable days and 26.10%in perturbed days.

False Positive Responses Optimization for Intrusion Detection System

In Intrusion Detection Systems (IDS), the operation costs represent one of the big challenges for researchers. They are apart from the IDS cost acquisition and they comprise the costs of maintenance, administration, response, running and errors reactions costs. In the present paper, we focus on the missed reactions which include False Positive (FP) and False Negative (FN) reactions. For that a new optimization cost model is proposed for IDS. This optimization proposes a minimal interval where the IDSs work optimally. In simulation, we found this interval as a trade-off between the damage costs and the FP.