The paradigm of the Internet of Things (IoT) and cloud-edge computing plays a significant role in future smart grids. The data-driven solution integrating the artificial intelligence functionalities brings novel methods to address the nontrivial task of economic dispatch in microgrids in the presence of uncertainties of renewable generations and loads. This article proposes a learning-based decision-making framework for the economic energy dispatch of an islanding microgrid based on the cloud-edge computing architecture. Cloud resources are utilized to solve the optimal dispatch decision sequences over historical operating patterns. It can be considered as a sample labeling process for the supervised training that can implement the complex mapping of input–output space through an advanced machine learning model. Then, the well-trained model can be adopted locally at edge computing devices keeping the long-term parameters unchanged for implement the real-time microgrid energy dispatch. The key benefit of the proposed solution is that it effectively avoids the prediction of multiple stochastic variables and the design of sophisticated regulation strategies or reward policy functions for real-time dispatch. The solution is extensively assessed through simulation experiments by the use of real data measurements for a set of operational scenarios and the numerical results validate the effectiveness and benefit of the proposed algorithmic solution.

Internet of things (IoT) technology provides the necessary foundation and support for smart city water management. To address the challenge of river pollution prevention and flood control requirements in the urban river system, this paper proposes a data-driven model to carry out the optimal operation scheduling of water diversion and drainage pumping stations in the presence of the complex hydrometeorological constraints. The proposed solution in the model predictive control (MPC) framework firstly adopts the long-short-term memory (LSTM) network through supervised learning from IoT data to simulate and predict the river flow dynamics and the water quality. Consequently, the optimal scheduling of controllable pumping stations to minimize the operational cost (e.g., the flocculant consumption) can be formulated as a stochastic optimization problem, whilst meeting the river flood control and water quality constraints. The particle swarm optimization (PSO) algorithm is further used to solve the above unit commitment optimization problem and obtain the optimal operational schedules of the water pumping units (e.g., startup time and working periods). The performance of the proposed optimal water pumping scheduling solution is evaluated through a field case study of the urban river diversion system and the numerical results clearly confirm its effectiveness and improved economic performance compared to the existing benchmark solution.

Accurate generation prediction at multiple time-steps is of paramount importance for reliable and economical operation of wind farms. This study proposed a novel algorithmic solution using various forms of machine learning techniques in a hybrid manner, including phase space reconstruction (PSR), input variable selection (IVS), K-means clustering and adaptive neuro-fuzzy inference system (ANFIS). The PSR technique transforms the historical time series into a set of phase-space variables combining with the numerical weather prediction (NWP) data to prepare candidate inputs. A minimal redundancy maximal relevance (mRMR) criterion based filtering approach is used to automatically select the optimal input variables for the multi-step ahead prediction. Then, the input instances are divided into a set of subsets using the K-means clustering to train the ANFIS. The ANFIS parameters are further optimized to improve the prediction performance by the use of particle swarm optimization (PSO) algorithm. The proposed solution is extensively evaluated through case studies of two realistic wind farms and the numerical results clearly confirm its effectiveness and improved prediction accuracy compared to benchmark solutions.