Modeling and Forecasting Energy Demand in TUCN Buildings

The accurate difference between the actual and predicted value of the future load demand defines the load forecasting. Load forecasting provides the most important information for power delivery and planning and plays an important role in energy management system. The aim of this paper is to model the electric baseline load profile, based on the predefined Key Performance Indicators (KPIs), in case of TUCN (Technical University of Cluj-Napoca) buildings and to demonstrate the economic and environmental benefits of controlling the consumption curve in Blocks of Buildings within several Demand Response programs

Modelling and evaluation of the baseline energy consumption and the key performance indicators in Technical University of Cluj-Napoca buildings within a demand response programme: A case study

Demand response (DR) programmes offer to customers the opportunity to reduce the power peak and the energy consumption in response to a price signal or financial incentive. Typically, the request to reduce peak demands is made for a specific time period on a specific day, which is referred to as a DR event. To predict a reference energy consumption level in case of different buildings or blocks of buildings within the Technical University of Cluj-Napoca, this study proposes an artificial intelligence enhanced energy profiling method and a more intuitive yet simple method for baseline determination, easy to understand, which allows all the interested parties to estimate the energy and economy savings after a DR event. Once the baseline electric load profile is established, the aim of this study is to calculate some predefined key performance indicators. The two baseline detection methods are compared with each other as a measure of DR event effectiveness. The study has been conducted to clearly demonstrate the economic and environmental benefits of controlling the aggregated load curve in blocks of buildings within several effectively applied DR programmes