Load Prediction Methods Using Machine Learning for Home Energy Management Systems Based on Human Behavior Patterns Recognition

Load forecasting can enhance the reliability and efficiency of operations in a home energy management system(HEMS).The rise of big data with machine learning in recent years makes it a potential solution.This paper proposes two new energy load forecasting methods,enhancing the traditional sequence to sequence long short-term memory(S2S-LSTM)model.Method 1 integrates S2S-LSTM with human behavior patterns recognition,implemented and compared by 3 types of algorithms:density based spatial clustering of applications with noise(DBSCAN),K-means and Pearson correlation coefficient(PCC).Among them,PCC is proven to be better than the others and suitable for a large number of residential customers.Method 2 further improves Method 1’s performance with a modified multi-layer Neural Network architecture,which is constituted by fully-connected,dropout and stable improved softmax layers.It optimizes the process of supervised learning in LSTM and improves the stability and accuracy of the prediction model.The performances of both proposed methods are evaluated on a dataset of 8-week electricity consumptions from 2337 residential customers.

Energy Management and Operational Control Methods for Grid Battery Energy Storage Systems

Energy storage is one of the key means for improving the flexibility,economy and security of power system.It is also important in promoting new energy consumption and the energy Internet.Therefore,energy storage is expected to support distributed power and the micro-grid,promote open sharing and flexible trading of energy production and consumption,and realize multi-functional coordination.In recent years,with the rapid development of the battery energy storage industry,its technology has shown the characteristics and trends for large-scale integration and distributed applications with multi-objective collaboration.As a grid-level application,energy management systems(EMS)of a battery energy storage system(BESS)were deployed in real time at utility control centers as an important component of power grid management.Based on the analysis of the development status of a BESS,this paper introduced application scenarios,such as reduction of power output fluctuations,agreement to the output plan at the renewable energy generation side,power grid frequency adjustment,power flow optimization at the power transmission side,and a distributed and niohile energy storage system at the power distribution side.The studies and application status of a BESS in recent years were reviewed.The energy management,operation control methods,and application scenes of large-scale BESSs were also examined in the study.

A comprehensive review of electrochemical hybrid power supply systems and intelligent energy managements for unmanned aerial vehicles in public services

The electric unmanned aerial vehicles (UAVs) are rapidly growing due to their abilities to perform some difficult or dangerous tasks as well as many public services including real-time monitoring, wireless coverage, search and rescue, wildlife surveys, and precision agriculture. However, the electrochemical power supply system of UAV is a critical issue in terms of its energy/power densities and lifetime for service endurance. In this paper, the current power supply systems used in UAVs are comprehensively reviewed and analyzed on the existing power configurations and the energy management systems. It is identified that a single type of electrochemical power source is not enough to support a UAV to achieve a long-haul flight;hence, a hybrid power system architecture is necessary. To make use of the advantages of each type of power source to increase the endurance and achieve good performance of the UAVs, the hybrid systems containing two or three types of power sources (fuel cell,battery, solar cell, and supercapacitor,) have to be developed. In this regard, the selection of an appropriate hybrid power structure with the optimized energy management system is critical for the efficient operation of a UAV. It is found that the data-driven models with artificial intelligence (AI) are promising in intelligent energy management. This paper can provide insights and guidelines for future research and development into the design and fabrication of the advanced UAV power systems.

An Intelligent Energy Management System for Large-Scale Charging of Electric Vehicles

The problem of large-scale charging of electric vehicles(EVs)with consumer-imposed charging deadlines is considered.An architecture for the intelligent energy management system(iEMS)is introduced.The iEMS consists of an admission control and pricing module,a scheduling module that determines the charging sequence,and a power dispatch module that draws power from a mix of storage,local renewable energy sources,and purchased power from the grid.A threshold admission and greedy scheduling(TAGS)policy is proposed to maximize operation profit.The performance of TAGS is analyzed and evaluated based on average and worst-case performance measures and the optimality of TAGS is established for some instances.Numerical simulations demonstrate that TAGS achieves noticeable performance gains over benchmark techniques.

A rank-based multiple-choice secretary algorithm for minimising microgrid operating cost under uncertainties

The increasing use of distributed energy resources changes the way to manage the electricity system.Unlike the traditional centralized powered utility,many homes and businesses with local electricity generators have established their own microgrids,which increases the use of renewable energy while introducing a new challenge to the management of the microgrid system from the mismatch and unknown of renewable energy generations,load demands,and dynamic electricity prices.To address this challenge,a rank-based multiple-choice secretary algorithm(RMSA)was proposed for microgrid management,to reduce the microgrid operating cost.Rather than relying on the complete information of future dynamic variables or accurate predictive approaches,a lightweight solution was used to make real-time decisions under uncertainties.The RMSA enables a microgrid to reduce the operating cost by determining the best electricity purchase timing for each task under dynamic pricing.Extensive experiments were conducted on real-world data sets to prove the efficacy of our solution in complex and divergent real-world scenarios.