As a special form of the Internet of Things, Smart Grid is an internet of both power and information, in which energy management is critical for making the best use of the power from renewable energy resources such as solar and wind, while efficient energy management is hinged upon precise forecasting of power generation from renewable energy resources. In this paper, we propose a novel least absolute shrinkage and selection operator (LASSO) and long short term memory (LSTM) integrated forecasting model for precise short-term prediction of solar intensity based on meteorological data. It is a fusion of a basic time series model, data clustering, a statistical model and machine learning. The proposed scheme first clusters data using k-means++. For each cluster, a distinctive forecasting model is then constructed by applying LSTM, which learns the non-linear relationships, and LASSO, which captures the linear relationship within the data. Simulation results with open-source datasets demonstrate the effectiveness and accuracy of the proposed model in short-term forecasting of solar intensity.

The smart grid (SG) has emerged as an important form of the Internet of Things. Despite the high promises of renewable energy in the SG, it brings about great challenges to the existing power grid due to its nature of intermittent and uncontrollable generation. In order to fully harvest its potential, accurate forecasting of renewable power generation is indispensable for effective power management. In this paper, we propose a least absolute shrinkage and selection operator (LASSO)-based forecasting model and algorithm for solar power generation forecasting. We compare the proposed scheme with two representative schemes with three real world datasets. We find that the LASSO-based algorithm achieves a considerably higher accuracy comparing to the existing methods, using fewer training data, and being robust to anomaly data points in the training data, and its variable selection capability also offers a convenient tradeoff between complexity and accuracy, which all make the proposed LASSO-based approach a highly competitive solution to forecasting of solar power generation.

Energy management is indispensable in the smart grid, which integrates more renewable energy resources, such as solar and wind. Because of the intermittent power generation from these resources, precise power forecasting has become very crucial to achieve efficient energy management. In this paper, we propose a novel adaptive learning hybrid model (ALHM) for precise solar intensity forecasting based on meteorological data. We first present a time-varying multiple linear model (TMLM) to capture the linear and dynamic property of the data, We then construct simultaneous confidence bands (SCB) for variable selection. Next we apply the genetic algorithm back propagation neural network (GABP) to learn the nonlinear relationships in the data. We further propose ALHM by integrating TMLM, GABP and the adaptive learning online hybrid algorithm (ALOHA). The proposed ALHM captures the linear, temporal and nonlinear relationships in the data, and keeps improving the predicting performance adaptively online as more data collected. Simulation results show that ALHM outperforms several benchmarks in both short-term and long-term solar intensity forecasting.

More electric aircraft (MEA) has become the trend of future advanced aircraft for its potential to be more efficient and reliable. The optimal power management, thus, plays an important role in MEA, especially when using hybrid energy storage systems (HESSs). In this article, we propose a novel adaptive online power management (AOPM) algorithm for MEA, which aims to minimize the power fluctuation of the generators based on the battery–supercapacitor HESS. The problem is first formulated as a constrained stochastic programming problem. We then present an online algorithm to approximately solve the problem using the Lyapunov optimization method, which does not require any statistics and future knowledge of the electricity demand. We further propose the AOPM algorithm for MEA by incorporating an adaptive strategy with the online algorithm. Trace-driven simulation results demonstrate the effectiveness, efficiency, and adaptability of the proposed power management algorithm for MEA.

In this paper, a hierarchical online distributed algorithm (HODA) is developed to achieve optimal energy management in inter-connected Microgrids (IMG). The energy management objectives include maximizing users’ utility, optimizing the output power of controllable generators, and keeping the system operating in an economic manner. We formulate the problem as an online least absolute shrinkage and selection operator (LASSO) problem, considering both reactive power and system operation characteristics. We then employ Averaging Fixed Horizon Control (AFHC) to solve the formulated problem under some mild assumptions on the uncertainties in renewable power generation and load demand. The alternating direction method of multipliers (ADMM) is adopted to decouple the coupled constraints. The proposed online algorithm is asymptotically optimal, since its solution converges to the offline optimal solution. The performance of the proposed algorithm is validated using data traces obtained from a real-world IMG system.

Due to the existence of predicting errors in the power systems, such as solar power, wind power and load demand, the economic performance of power systems can be weakened accordingly. In this paper, we propose an adaptive solar power forecasting (ASPF) method for precise solar power forecasting, which captures the characteristics of forecasting errors and revises the predictions accordingly by combining data clustering, variable selection, and neural network. The proposed ASPF is thus quite general, and does not require any specific original forecasting method. We first propose the framework of ASPF, featuring the data identification and data updating. We then present the applied improved k-means clustering, the least angular regression algorithm, and BPNN, followed by the realization of ASPF, which is shown to improve as more data collected. Simulation results show the effectiveness of the proposed ASPF based on the trace-driven data.