An Improved Non-Schedulable Load Forecasting Strategy for Enhancing the Performance of the Energy Management in a Nearly Zero Energy Building

This paper proposes an improved non-schedulable load forecasting (NLF) technique that can be utilized to enhance the performance of the energy management system (EMS) in a nearly zero energy building (nZEB). The suggested NLF is based on a long short-term memory (LSTM) framework in conjunction with a semi-supervised clustering (SSC) technique, considering the most important features which may affect the energy consumption of the non-schedulable appliances (NAs), i.e. number and identity of residents, energy consumption, weather, temperature, humidity, outdoor irradiance, correlation with other loads, day of the week and holidays. The SSC algorithm is utilized to fill the uncompleted information for the residents' presence in the house and its output constitutes one of the inputs of the LSTM based technique which provides as output a set of forecasting sequences of the NAs' energy consumption. Unlike the published techniques, the proposed NLF method is not only based on the modeling of the residents' preferences and habits, but it considers them as variables which affect the nZEB's microgrid and EMS performance. Therefore, it predicts the residents' behavior considering its interdependence with the nZEB's microgrid, which can considerably contribute to the enhancement of the EMS effectiveness and performance. For the implementation of the proposed NLF, no additional hardware is required, but only amendments in the EMS to consider the NLF's outcomes. To validate the effectiveness of the proposed NLF, selective Hardware-in-the-Loop results from a real nZEB are presented.

Automated summary

This paper proposes an improved non-schedulable load forecasting technique based on LSTM framework and semi-supervised clustering, considering various factors to predict the energy consumption of non-schedulable appliances and enhance the EMS performance in nZEB. Unlike existing techniques, the proposed method not only models residents' preferences and habits, but also considers them as variables affecting the system's performance, effectively predicting residents' behavior and enhancing the EMS effectiveness.