Shortterm Electricity Load Forecasting Using a MapReducebased Elman Networks with Coarsegrained Parallel Genetic Optimization How to make full use of highdimensional massive power data to improve the accuracy and efficiency of shortterm electrical load forecasting is a challenging problem to be solved. This paper discusses a parallel load forecasting technique with an Elman Neural Network(ENN) based on the MapReduce(MR) programming model. Specially, a CoarseGrained Parallel Genetic Algorithm(CPGA) is introduced into the process of ENN training for obtaining the promising weights and thresholds. In the framework of MR, a parallel loadforecasting model of MR CPGAENN is established. Case study with different scenario based electrical load data sets and meteorological information confirm that both the accuracy and efficiency of the developed model are superior to the conventional models compared.

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.