A Stacking MultiLearning Ensemble Model for Predicting Near Real Time Energy Consumption Demand of Residential Buildings The aim of this paper is to present a novel energy consumption forecasting solution for predicting energy demand at the level of residential buildings based on their historical consumption profile for a seamless integration with the sessionbased Energy Markets developed within Smart Energy Grids that integrate renewable energy sources. To overcome the drawbacks and lack of accuracy of existing prediction models, a stacked multilearning ensemble model is proposed combining Gradient Boosting Regression, MultiLayer Neural Networks and Long Short Term Memory Networks followed by a Linear Regressor for forecasting residential energy demands both at individual and aggregated levels. The proposed ensemble predictor is evaluated using the openaccess UKDALE dataset containing historical energy traces for 5 households spreading over several years, obtaining a best MAPE of 1.59%, a RMSE of 6. 19 kWh and a MAE of 5. 60 kWh on the aggregated dataset, proving the high accuracy and stability of the proposed solution as well as the feasibility of using ensemble models for residential building energy demand forecast for integration with sessionbased energy marketplaces.

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.