Short-Term Load Forecasting of building electricity consumption using NARX Neural Networks model

• Published in: 2021 6th International Conference on Smart and Sustainable Technologies (SpliTech) pp. 1 - 6
• Main Authors: Zuazo, Irati Zapirain, Boussaada, Zina, Aginako, Naiara, Curea, Octavian, Camblong, Haritza, Sierra, Basilio
• Format: Conference Proceeding
• Language: English
• Published: University of Split, FESB 08.09.2021
• Subjects: Artificial Neural Networks; building electric consumption forecasting; Buildings; Collective self-consumption; Data models; distributed generation; Heating systems; Load forecasting; Nonlinear Autoregressive Exogenous; Predictive models; Short-Term Load Forecasting; Ventilation; Windows
• DOI: 10.23919/SpliTech52315.2021.9566440

Electric grid, as we nowadays know it, is undergoing a significant transformation. What we are now witnessing is an undoubted change of trend towards a decentralized and decarbonized electric grid, where the electric generation based on local resources will take on special relevance. In this context, the encouragement of collective self-consumption becomes one of the key issues when it comes to taking steps forward to this end. One of the aspects that will contribute to this aim is the development of a consumption-forecasting tool. Hence, a load-forecasting model based on NARX Neural Network is proposed in the following paper. The prediction of the next day (24h) load profile of an individual building is carried out aiming an optimal management of the flexible loads so to achieve the maximum self-consumption. To ensure a consistent behavior of the NARX Neural Network model, identification and removing of outliers, together with data normalization and fixing common time interval has been carried out. The first results of the research are promising, being obtained a 17,6% MAPE in NARX and 25,19% with LSTM model, both evaluated during a regular week on winter in adverse conditions.