Publikationer

The growing population and demand for new public buildings contribute to increased energy consumption and greenhouse emienisdans. In Sweden, the largest amount of energy is consumed in school buildings, i.e., where schools form the highest number of public properties (30 million m ² ). In total, schools consumed 4 222 GWh of district heating and about 3 GWh of electricity for heating and other purposes in 2020. These figures lead to the realization of the need to apply effective measures to meet the European Green Deal target for 2030. Accurately forecasting energy usage is important for all stakeholders to conduct economic analysis and optimize decision-making. It is equally important in maintenance operations to allocate resources and enable the staff and students to adjust their behaviours and address the issues in buildings where peak forecasts occur. This paper develops and evaluates a power and district heating consumption for a single day and multiple days forecasting using Multivariate Recurrent Neural Network (RNN) -Long-Short term memory (LSTM) and convolutional neural networks (CNNs) and Autoencoders (AE), using daily real consumption data of six public schools provided by Skelefteå municipality in Sweden. The experimental results demonstrate that the hybrid model CNN-LSTM has achieved good accuracy compared to others, with RMSE and nRMSE error between 18%-25% and 5%-6% for electricity, respectively, and between 20%-30% RMSE and 5% nRMSE for district heating.

The development of accurate energy prediction models plays a significant role in achieving sustainability in smart cities. However, stakeholders such as municipalities face the problem of creating individual energy forecasting models for multiple building fleets which leads to an increased amount of computational resources and time spent to prepare each model. This research proposes a method using Hierarchical clustering with dynamic time warping to group similar buildings according to their consumption values and the integration of transfer Learning (TL) to share the model weights from a source building to other target buildings. Different TL models using only 20%, 40%, and 60% of the target data were tested against a standard workflow without TL for predicting electricity and district heating for several school buildings using a Multivariate LSTM model. The results show a small variation between the TL and the standard models; when trained on only 40% of the data, the models achieved an average of 0.24% RMSE improvement for district heating and a 1.23% for electricity, indicating a potential for reduced data requirements without sacrificing predictive accuracy and demonstrating TL’s efficiency to streamline the energy forecasting process for building fleets.