Shortterm Forecasting of Electricity Consumption in Buildings for Efficient and Optimal Distributed Energy Management The electricity consumption profile of buildings are different from the typical load curves that represent the electricity consumption of large systems at the national or regional level. The electricity demand in buildings is many times lower than the region or nationwide demands. It is also much more volatile and stochastic, meaning that the conventional tools are not effective enough for straightforward application at a building demand level. In this paper, an integrated approach consisting of HilbertHuang Transform (HHT), Regrouping Particle Swarm Optimization (RegPSO) and Adaptive NeuroFuzzy Inference System (ANFIS) is devised for 24hahead prediction of electric power consumption in buildings. The forecasts are used as input information for smart decisions of distributed energy management systems that control the optimal bidding and scheduling of energy resources for building energy communities. The effectiveness of the proposed forecasting approach is demonstrated using actual electricity demand data from various buildings in the Otaniemi area of Espoo, Finland. The prediction performance of the proposed approach for various building types (energy customer clusters), has been examined and statistical comparisons are presented. The prediction results are presented for future days with a onehour time interval. The validation results demonstrate that the approach is able to forecast the buildingsxe2x80x99 electricity demand with smaller error, outperforming five other approaches, and in reasonably short computation times.

Dayahead Prediction of Building District Heat Demand for Smart Energy Management and Automation in Decentralized Energy Systems This paper proposes an Artificial Intelligence (AI) based datadriven approach to forecast heat demand for various customer types in a District Heating System (DHS). The proposed dayahead forecasting approach is based on a hybrid model consisting of Imperialistic Competitive Algorithm (ICA) and Support Vector Machine (SVM). The model is built using two years (2015  2016) of hourly data from various buildings in the Otaniemi area of Espoo, Finland. Dayahead forecast models are also developed using Persistence and four other AI based techniques. Comparative forecasting performance analysis among these techniques was performed. The proposed ICASVM heat demand forecasting model is tested and validated using an outofsample oneyear (2017) hourly data of the buildingsxe2x80x99 district heat consumption. The prediction results are presented for the outofsample testing days in a onehour time interval. The validation results demonstrate that the devised model is able to predict the buildingsxe2x80x99 heat demand with an improved accuracy and short computation time. Moreover, the proposed model demonstrates outperformed prediction accuracy improvement, compared to the other five evaluated models.