Home Network and Digital Rights Management Technology

The home network is a major concern for the growth of digital and information society. Yet, how to guarantee the security of its digital content and protect the legal benefits for each section of the value chain becomes a crucial "bottleneck" in the home network development. The Digital Rights Management (DRM) technology provides total solution for usage, storage, transfer, and tracing the digital contents and rights. Its basic features are systematic and controllability. Considering the growth of the new media and services and the requirements of the Intellectual Property Rights (IPR) protection in a home network, it's necessary to solve consistency problems in usage, storage, and transfer of contents and rights. In addition, it is inevitable to conduct researches of key techniques such as end-to-end secure transmission, conditional access and play, and right description.

Home Energy Management System Using NILM and Low-Cost HAN

Home energy management systems （HEMs） are used to provide comfortable life for consumers as well as to save energy. An essential component of HEMs is a home area network （HAN） that is used to remotely control the electric devices at homes and buildings. Although HAN prices have dropped in ~ecent years but they are still expensive enough to prohibit a mass scale deployments. In this paper, a very low cost alternative to the expensive HANs is presented. We have applied a combination of non-intrusive load monitoring （NILM） and very low cost one-way HAN to develop a HEM. By using NILM and machine learning algorithms we find the status of devices and their energy consumption from a central meter and communicate with devices through the one-way HAN. The evaluations show that the proposed machine learning algorithm for NILM achieves up to 99% accuracy in certain cases. On the other hand our radio frequency （RF）-based one-way HAN achieves a range of 80 feet in all settings.

Hybrid-integer algorithm for a multi-objective optimal home energy management system

Most of the energy produced in the world is consumed by commercial and residential buildings.With the growth in the global economy and world demographics,this energy demand has become increasingly important.This has led to higher unit electricity prices,frequent stresses on the main electricity grid and carbon emissions due to inefficient energy management.This paper presents an energy-consumption management system based on time-shifting of loads according to the dynamic day-ahead electricity pricing.This simultaneously reduces the electricity bill and the peaks,while maintaining user comfort in terms of the operating waiting time of appliances.The proposed optimization problem is formulated mathematically in terms of multi-objective integer non-linear programming,which involves constraints and consumer preferences.For optimal scheduling,the management problem is solved using the hybridization of the particle swarm optimization algorithm and the branch-and-bound algorithm.Two techniques are proposed to manage the trade-off between the conflicting objectives.The first technique is the Pareto-optimal solutions classification using supervised learning methods.The second technique is called the lexicographic method.The simulations were performed based on residential building energy consumption,time-of-use pricing(TOU)and critical peak pricing(CPP).The algorithms were implemented in Python.The results of the current work show that the proposed approach is effective and can reduce the electricity bill and the peak-to-average ratio(PAR)by 28% and 49.32%,respectively,for the TOU tariff rate,and 48.91% and 47.87% for the CPP tariff rate by taking into account the consumer’s comfort level.

HVAC energy cost minimization in smart grids: A cloud-based demand side management approach with game theory optimization and deep learning

In this paper, we present a novel cloud-based demand side management (DSM) optimization approach for the cost reduction of energy usage in heating, ventilation and air conditioning (HVAC) systems in residential homes at the district level. The proposed approach achieves optimization through scheduling of HVAC energy usage within permissible bounds set by house users. House smart home energy management (SHEM) devices are connected to the utility/aggregator via a dedicated communication network that is used to enable DSM. Each house SHEM can predict its own HVAC energy usage for the next 24 h using minimalistic deep learning (DL) prediction models. These predictions are communicated to the aggregator, which will then do day ahead optimizations using the proposed game theory (GT) algorithm. The GT model captures the interaction between aggregator and customers and identifies a solution to the GT problem that translates into HVAC energy peak shifting and peak reduction achieved by rescheduling HVAC energy usage. The found solution is communicated by the aggregator to houses SHEM devices in the form of offers via DSM signals. If customers’ SHEM devices accept the offer, then energy cost reduction will be achieved. To validate the proposed algorithm, we conduct extensive simulations with a custom simulation tool based on GridLab-D tool, which is integrated with DL prediction models and optimization libraries. Results show that HVAC energy cost can be reduced by up to 36% while indirectly also reducing the peak-to-average (PAR) and the aggregated net load by up to 9.97%.

Optimal gateway deployment under different queuing mechanisms in smart grid

By optimizing the network topology, this paper proposes a newmethod of queuing theory clustering algorithm based on dynamic programming in a home energy management system（ HEMS）. First, the total cost of the HEMS system is divided into two parts, the gateway installation cost and the data transmission cost. Secondly, through comparing two kinds of different queuing theories, the cost problem of the HEMS is converted into the problem of gateway deployment. Finally, a machine-to-machine（ M2M） gateway configuration scheme is designed to minimize the cost of the system. Simulation results showthat the cost of the HEMS system mainly comes from the installation cost of the gateways when the gateway buffer space is large enough. If the gateway buffer space is limited, the proposed queue algorithm can effectively achieve optimal gateway setting while maintaining the minimal cost of the HEMS at desired levels through marginal analyses and the properties of cost minimization.

Optimization Strategy Based on Deep Reinforcement Learning for Home Energy Management

With the development of a smart grid and smart home,massive amounts of data can be made available,providing the basis for algorithm training in artificial intelligence applications.These continuous improving conditions are expected to enable the home energy management system(HEMS)to cope with the increasing complexities and uncertainties in the enduser side of the power grid system.In this paper,a home energy management optimization strategy is proposed based on deep Q-learning(DQN)and double deep Q-learning(DDQN)to perform scheduling of home energy appliances.The applied algorithms are model-free and can help the customers reduce electricity consumption by taking a series of actions in response to a dynamic environment.In the test,the DDQN is more appropriate for minimizing the cost in a HEMS compared to DQN.In the process of method implementation,the generalization and reward setting of the algorithms are discussed and analyzed in detail.The results of this method are compared with those of Particle Swarm Optimization(PSO)to validate the performance of the proposed algorithm.The effectiveness of applied data-driven methods is validated by using a real-world database combined with the household energy storage model.

Integrated Optimization of Smart Home Appliances with Cost-effective Energy Management System

Smart grid enables consumers to control and sched-ule the consumption pattern of their appliances,minimize energy cost,peak-to-average ratio(PAR)and peak load demand.In this paper,a general architecture of home energy management system(HEMS)is developed in smart grid scenario with novel restricted and multi-restricted scheduling method for the residen-tial customers.The optimization problem is developed under the time of use pricing(TOUP)scheme.To optimize the formulated problem,a powerful meta-heuristic algorithm called grey wolf optimizer(GWO)is utilized,which is compared with particle swarm optimization(PSO)algorithm to show its effectiveness.A rooftop photovoltaic(PV)system is integrated with the system to show the cost effectiveness of the appliances.For analysis,eight different cases are considered under various time scheduling algorithms.

Mixed Deep Reinforcement Learning Considering Discrete-continuous Hybrid Action Space for Smart Home Energy Management

This paper develops deep reinforcement learning(DRL)algorithms for optimizing the operation of home energy system which consists of photovoltaic(PV)panels,battery energy storage system,and household appliances.Model-free DRL algorithms can efficiently handle the difficulty of energy system modeling and uncertainty of PV generation.However,discretecontinuous hybrid action space of the considered home energy system challenges existing DRL algorithms for either discrete actions or continuous actions.Thus,a mixed deep reinforcement learning(MDRL)algorithm is proposed,which integrates deep Q-learning(DQL)algorithm and deep deterministic policy gradient(DDPG)algorithm.The DQL algorithm deals with discrete actions,while the DDPG algorithm handles continuous actions.The MDRL algorithm learns optimal strategy by trialand-error interactions with the environment.However,unsafe actions,which violate system constraints,can give rise to great cost.To handle such problem,a safe-MDRL algorithm is further proposed.Simulation studies demonstrate that the proposed MDRL algorithm can efficiently handle the challenge from discrete-continuous hybrid action space for home energy management.The proposed MDRL algorithm reduces the operation cost while maintaining the human thermal comfort by comparing with benchmark algorithms on the test dataset.Moreover,the safe-MDRL algorithm greatly reduces the loss of thermal comfort in the learning stage by the proposed MDRL algorithm.

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.

Optimal Home Energy Management System with Mixed Types of Loads

This paper presents a novel home area energy management system(HEMS)for smart homes with different load profiles installed with photovoltaic generation,energy storage,and DC demand.The developed HEMS is shown to optimize the utilization of local renewables while minimizing energy waste between AC and DC conversions and between storage charging and discharging.Previous studies on HEMS have not considered the impact of load types.In this study,the performance of the proposed HEMS is demonstrated on different smart homes with and without electric heating.A comparative study is carried out to investigate battery behavior,characteristics of AC and DC conversion,and benefits that customers receive.A sensitivity analysis is also conducted to discuss the effects from varied energy storage capacities,AC/DC conversion efficiencies,and PV output.The results show that cost reduction in energy bills can be greatly impacted by load profiles,and customers with electric heating load coupled with sufficiently large energy storage would receive the most reduction in their energy bills.

Distributionally robust optimization of home energy management system based on receding horizon optimization

This paper investigates the scheduling strategy of schedulable load in home energy management system(HEMS)under uncertain environment by proposing a distributionally robust optimization(DRO)method based on receding horizon optimization(RHO-DRO).First,the optimization model of HEMS,which contains uncertain variable outdoor temperature and hot water demand,is established and the scheduling problem is developed into a mixed integer linear programming(MILP)by using the DRO method based on the ambiguity sets of the probability distribution of uncertain variables.Combined with RHO,the MILP is solved in a rolling fashion using the latest update data related to uncertain variables.The simulation results demonstrate that the scheduling results are robust under uncertain environment while satisfying all operating constraints with little violation of user thermal comfort.Furthermore,compared with the robust optimization(RO)method,the RHO-DRO method proposed in this paper has a lower conservation and can save more electricity for users.

Deep reinforcement learning for home energy management system control

The use of machine learning techniques has been proven to be a viable solution for smart home energy man-agement.These techniques autonomously control heating and domestic hot water systems,which are the most relevant loads in a dwelling,helping consumers to reduce energy consumption and also improving their comfort.Moreover,the number of houses equipped with renewable energy resources is increasing,and this is a key ele-ment for energy usage optimization,where coordinating loads and production can bring additional savings and reduce peak loads.In this regard,we propose the development of a deep reinforcement learning(DRL)algorithm for indoor and domestic hot water temperature control,aiming to reduce energy consumption by optimizing the usage of PV energy production.Furthermore,a methodology for a new dynamic indoor temperature setpoint definition is presented,thus allowing greater flexibility and savings.The results show that the proposed DRL al-gorithm combined with the dynamic setpoint achieved on average 8%of energy savings compared to a rule-based algorithm,reaching up to 16%of savings over the summer period.Moreover,the users’comfort has not been compromised,as the algorithm is calibrated to not exceed more than 1%of the time out the specified temperature setpoints.Additional analysis shows that further savings could be achieved if the time out of comfort is increased,which could be agreed according to users’needs.Regarding demand side management,the DRL control shows efficiency by anticipating and delaying actions for a PV self-consumption optimization,performing over 10%of load shifting.Finally,the renewable energy consumption is 9.5%higher for the DRL-based model compared to the rule-based,which means less energy consumed from the grid.

Graph representation learning-based residential electricity behavior identification and energy management

It is important to achieve an efficient home energy management system(HEMS)because of its role in promoting energy saving and emission reduction for end-users.Two critical issues in an efficient HEMS are identification of user behavior and energy management strategy.However,current HEMS methods usually assume perfect knowledge of user behavior or ignore the strong correlations of usage habits with different applications.This can lead to an insuffi-cient description of behavior and suboptimal management strategy.To address these gaps,this paper proposes non-intrusive load monitoring(NILM)assisted graph reinforcement learning(GRL)for intelligent HEMS decision making.First,a behavior correlation graph incorporating NILM is introduced to represent the energy consumption behavior of users and a multi-label classification model is used to monitor the loads.Thus,efficient identification of user behavior and description of state transition can be achieved.Second,based on the online updating of the behavior correlation graph,a GRL model is proposed to extract information contained in the graph.Thus,reliable strategy under uncer-tainty of environment and behavior is available.Finally,the experimental results on several datasets verify the effec-tiveness of the proposed model.

A Dual-spline Approach to Load Error Repair in a HEMS Sensor Network

In a home energy management system(HEMS),appliances are becoming diversified and intelligent,so that certain simple maintenance work can be completed by appliances themselves.During the measurement,collection and transmission of electricity load data in a HEMS sensor network,however,problems can be caused on the data due to faulty sensing processes and/or lost links,etc.In order to ensure the quality of retrieved load data,different solutions have been presented,but suffered from low recognition rates and high complexity.In this paper,a validation and repair method is presented to detect potential failures and errors in a domestic energy management system,which can then recover determined load errors and losses.A Kernel Extreme Learning Machine(K-ELM)based model has been employed with a Radial Basis Function(RBF)and optimised parameters for verification and recognition;whilst a Dual-spline method is presented to repair missing load data.According to the experiment results,the method outperforms the traditional B-spline and Cubic-spline methods and can effectively deal with unexpected data losses and errors under variant loss rates in a practical home environment.

Analysis of Dynamic Appliance Flexibility Considering User Behavior via Non-intrusive Load Monitoring and Deep User Modeling

The research on non-intrusive load monitoring(NILM)and the growing deployment of home energy manage-ment system(HEMS)have made it possible for households to have a detailed understanding of their power usage and to make appliances participate in demand response(DR)programs.Appliance flexibility analysis helps the HEMS dispatching appli-ances to participate in DR programs without violating user’s comfort level.In this paper,a dynamic appliance flexibility analysis approach using the smart meter data is presented.In the training phase,the smart meter data is preprocessed by NILM to obtain user’s appliances usage behaviors,which is used to train the user model.During operation,the NILM is used to infer recent appliances usage behaviors,and then the user model predicts user’s appliances usage behaviors in the DR period considering long-term behaviors dependences,correlations between appliances and temporal information.The flexibility of each appliance is calculated based on the appliance characteristics as well as the predicted user’s appliances usage behaviors caused by the control of the appliance.The HEMS can choose the appliance with high flexibility to participate in the DR programs.The case study demonstrates the performance of the user model and illustrates how the appliance flexibility analysis is performed using a real-world case.

Probabilistic Assessment of Impact of Flexible Loads Under Network Tariffs in Low-voltage Distribution Networks

Given the historically static nature of low-voltage networks, distribution network companies do not possess the tools for dealing with an increasingly variable demand due to the high penetration of distributed energy resources (DERs). Within this context, this paper proposes a probabilistic framework for tariff design that minimises the impact of DER on network performance, stabilises the revenue of network company, and improves the equity of network cost allocation. To deal with the lack of customers’ response, we also show how DER-specific tariffs can be complemented with an automated home energy management system (HEMS) that reduces peak demand while retaining the desired comfort level. The proposed framework comprises a nonparametric Bayesian model which statistically generates synthetic load and PV traces, a hot-water-use statistical model, a novel HEMS to schedule customers’ controllable devices, and a probabilistic power flow model. Test cases using both energy- and demand-based network tariffs show that flat tariffs with a peak demand component reduce the customers’ cost, and alleviate network constraints. This demonstrates, firstly, the efficacy of the proposed tool for the development of tariffs that are beneficial for the networks with a high penetration of DERs, and secondly, how customers’ HEM systems can be part of the solution.

Comparison of optimizers for model predictive thermal control of buildings

Considering recent developments in the energy sector,further reduction of electricity cost and flattening of the electric power demand curve are needed.We have focused on an autonomous electric heater control system that can easily be implemented in existing buildings without strict comfort requirements.Examples are winter heating of warehouses and vacation homes,and heat drying of buildings under construction.We have set up a system that typically reduces electricity cost by about 40%on the basis of automatic weather and real time pricing forecasts.The system uses the building as an energy reservoir over periods with high electricity cost.Using a model predictive control system,we compare use of a genetic algorithm,a particle swarm optimization,and a neural network for heater control,all working in a closed loop to reduce the influence of modeling errors.We have simulated the performance of the systems using realistic data and found that all three optimizers give about the same performance,varying only a few percent in efficiency.However,the computational and memory requirements of the neural network are much lower than for the other optimizers,so it is preferable for use with inexpensive microcontrollers.We carried out a full-scale experiment at a residential house and found agreement with simulation results.