Model Predictive Control for Real-Time Residential Energy Scheduling under Uncertainties

Hosseini, Seyed Mohsen; Carli, Raffaele; Dotoli, Mariagrazia

doi:10.1109/smc.2018.00242

Cited by 27 publications

(30 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Due to the limitation of acquisition devices and public data set, the proposed method can only be applied to consumers in power grid. For the prosumers mentioned in several recent works [24][25][26], this will be studied in the next work.…”

Section: Introductionmentioning

confidence: 99%

Multi-Label Classification Based on Random Forest Algorithm for Non-Intrusive Load Monitoring System

Gao

Jiao

2019

Processes

View full text Add to dashboard Cite

Non-intrusive load monitoring (NILM) is an effective method to optimize energy consumption patterns. Since the concept of NILM was proposed, extensive research has focused on energy disaggregation or load identification. The traditional method is to disaggregate mixed signals, and then identify the independent load. This paper proposes a multi-label classification method using Random Forest (RF) as a learning algorithm for non-intrusive load identification. Multi-label classification can be used to determine which categories data belong to. This classification can help to identify the operation states of independent loads from mixed signals without disaggregation. The experiments are conducted in real environment and public data set respectively. Several basic electrical features are selected as the classification feature to build the classification model. These features are also compared to select the most suitable features for classification by feature importance parameters. The classification accuracy and F-score of the proposed method can reach 0.97 and 0.98, respectively.

show abstract

Section: Introductionmentioning

confidence: 99%

Multi-Label Classification Based on Random Forest Algorithm for Non-Intrusive Load Monitoring System

Gao

Jiao

2019

Processes

View full text Add to dashboard Cite

show abstract

“…Integrating storage units, in the literature [16][17][18], demand side management techniques are shown with consumers, producers, prosumers, as well as users with storage capability. In the work of [16], the model proposed can work with controllable appliances, RES, dispatchable energy resources, and energy storage systems.…”

Section: Related Literaturementioning

confidence: 99%

“…These authors report that with the increasing of the home smartness, the reduction of the energy cost is proven. The works of [17] and [18] take into account the uncertainties. Namely, in the work of [17], the model introduced manages energy storage systems and distributed generation, working with wind and solar generation uncertainties.…”

Section: Related Literaturementioning

confidence: 99%

“…Namely, in the work of [17], the model introduced manages energy storage systems and distributed generation, working with wind and solar generation uncertainties. In another paper [18], a predictive control model for real-time is presented, considering deferrable and non-deferrable load, RES, and energy storage systems with dynamic profiles. These studies were able to reduce the total user load in a sustainable way.…”

Section: Related Literaturementioning

confidence: 99%

See 1 more Smart Citation

Demand Response and Distributed Generation Remuneration Approach Considering Planning and Operation Stages

Silva¹,

Faria²,

Vale

2019

Energies

View full text Add to dashboard Cite

The need for new business models to replace existing ones, soon obsolete, is a subject often discussed among researchers in the area. It is essential to find a practical solution that includes the concepts of demand response and distributed generation in the energy markets, these being the future of the electricity grid. It is believed that these resources can bring advantages to the operation of the system, namely increasing technical efficiency. However, one of the problems is the aggregation of small resources as a result of the associated uncertainties. The authors propose a business model with three main phases used in planning: optimal scheduling, aggregation, and remuneration. In this paper, a new phase was added, the classification, with the main purpose of assisting the aggregator of these small resources in operating situations. The focus is on the fair remuneration of participants in the management of the market, in addition to minimizing operating costs. After testing four different remuneration methods, it was proved that the method proposed by the authors obtained better results, proving the viability of the proposed model.

show abstract

“…The suggested control signal (T a ) can be incorporated into the HVAC system by feeding it as a set-point to the HVAC controller. The sequence of predicted thermal sensation is crucial in the optimisation (cost function) of the control (manipulated) variables [35,36]. In the presented approach, we have considered the air temperature as the only manipulated variable; however, more HVAC-related variables can be added to the optimisation step (e.g., ventilation rate and energy consumption).…”

Section: The Gpc Algorithmmentioning

confidence: 99%

Model-Based Monitoring of Occupant’s Thermal State for Adaptive HVAC Predictive Controlling

2019

View full text Add to dashboard Cite

Conventional indoor climate design and control approaches are based on static thermal comfort/sensation models that view the building occupants as passive recipients of their thermal environment. Recent advances in wearable sensing technologies and their generated streaming data are providing a unique opportunity to understand the user's behaviour and to predict future needs. Estimation of thermal comfort is a challenging task given the subjectivity of human perception; this subjectivity is reflected in the statistical nature of comfort models, as well as the plethora of comfort models available. Additionally, such models are using not-easily or invasively measured variables (e.g., core temperatures and metabolic rate), which are often not practical and undesirable measurements. The main goal of this paper was to develop dynamic model-based monitoring system of the occupant's thermal state and their thermoregulation responses under two different activity levels. In total, 25 participants were subjected to three different environmental temperatures at two different activity levels. The results have shown that a reduced-ordered (second-order) multi-inputs-single-output discrete-time transfer function (MISO-DTF), including three input variables (wearables), namely, aural temperature, heart rate, and average skin heat-flux, is best to estimate the individual's metabolic rate (non-wearable) with a mean absolute percentage error of 8.7%. A general classification model based on a least squares support vector machine (LS-SVM) technique is developed to predict the individual's thermal sensation. For a seven-class classification problem, the results have shown that the overall model accuracy of the developed classifier is 76% with an F1-score value of 84%. The developed LS-SVM classification model for prediction of occupant's thermal sensation can be integrated in the heating, ventilation and air conditioning (HVAC) system to provide an occupant thermal state-based climate controller. In this paper, we introduced an adaptive occupant-based HVAC predictive controller using the developed LS-SVM predictive classification model. Processes 2019, 7, 720 2 of 15Human thermal sensation mainly depends on the human body temperature (core body temperature), which is a function of sets of comfort factors [4,5]. These comfort factors include indoor environmental factors, namely the mean air temperature around the body, relative air velocity around the body, humidity, and the mean radiant temperature to the body [5]. Additionally, some personal (individual-related) factors, namely, metabolic rate or internal heat production in the body, which vary with the activity level and clothing thermal-physical properties (such as clothing insulation and vapour clothing resistance), are included. It should be mentioned that the individual thermal perception is deepening, as well, on psychological factors, including naturalness (an environment where the people tolerate wide changes of the physical environment), expectations and short/long-term expe...

show abstract

Model Predictive Control for Real-Time Residential Energy Scheduling under Uncertainties

Cited by 27 publications

References 19 publications

Multi-Label Classification Based on Random Forest Algorithm for Non-Intrusive Load Monitoring System

Multi-Label Classification Based on Random Forest Algorithm for Non-Intrusive Load Monitoring System

Demand Response and Distributed Generation Remuneration Approach Considering Planning and Operation Stages

Model-Based Monitoring of Occupant’s Thermal State for Adaptive HVAC Predictive Controlling

Contact Info

Product

Resources

About