Top PDF Intelligent energy management system in buildings

Intelligent energy management system in buildings

Intelligent energy management system in buildings

Traditional recommender systems mostly focus on recommending the most relevant items to individual users and do not take into consideration any contextual information. Such as the ones based on content-based and collaborative filtering, which tend to use relatively simple user models (Adomavicius & Tuzhilin, 2015). This type of recommender system deal with situations that only two types are entities are used, users and items, and these data are not ordered in contexts to be used to provide the recommendations. Meanwhile, considering the contextual information clearly will improve the efficiency of the recommendations for every user. This way, the Context-Aware Recommender Systems (CARS) addresses the concept of the recommender system, which considers the different contexts of the input data and based their recommendations on this context. For example, in (Hosseinzadeh Aghdam, 2019) presents a novel hierarchical hidden Markov model to identify changes in the user’s preferences over time by modeling the latent context of users. Or in (Marreiros, Santos, Ramos, & Neves, 2010) where has been developed a context-aware emotion-based model to design intelligent agents for group decision-making processes. Also, (Zheng & Jose, 2019) propose a novel recommender for context-aware recommendations in which estimates the user preferences by sequential predictions. In (Rodriguez-Fernandez et al., 2019) has been introduced a new model to estimate the expected prices that can be achieved in bilateral contracts under a specific context, enabling adequate risk management in the negotiation process.
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Intelligent optimization and management system for renewable energy systems using multi-agent

Intelligent optimization and management system for renewable energy systems using multi-agent

Due to their complexities and geographical distribution, HRES cannot be easily managed by centralized systems. Intelligent integrated systems must manage the production of renewable energies or help reduce the consumption of the various users of the network. Distributed artificial intelligence and more particularly multi-agent systems appear as an adequate means of solving problems related to autonomous systems. An agent is all that can be seen as perceiving its environment by means of sensors and acting on this environment using effectors autonomously. This definition gives a very general overview on the definition of agents. In [22], Wooldridge and Jennings define an agent as a system, software or not, with the following properties:
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Intelligent Air Conditioning System For Energy Management Using Wireless Control

Intelligent Air Conditioning System For Energy Management Using Wireless Control

The figure shows the basic block diagram of the proposed system. This may avoids the difficulties of the present system, so that power consumption can be reduced. The difference of this proposed system from the commonly used system is that it has a central monitoring system and it uses

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Wireless Intelligent Power Management System

Wireless Intelligent Power Management System

Rising costs in the power industry have focused upon ways to control or adjust the units consumed during particular hours of the day. The economic solution to this problem is to implement peak load pricing (PLP).it is considered that the Power is the instantaneously measured quantity whereas the Energy is calculated by integrating power for a unit period of time. The maximum instantaneous power consumed over specified period of time is thus considered as the Maximum Demand. The consumer is billed on the basis of this maximum demand by the distribution utilities in his area of supply and thus this is considered to be the vital important factor in the electricity bill. Rather than measure truly instantaneous values actual energy over a short window of time is measured and then divided by the length of the interval to arrive at an effective peak value for the interval.
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Intelligent Energy Saving System

Intelligent Energy Saving System

It has been reported that natural ventilation in buildings functions best in warm summer areas, such as Europe (Roth, Dieckmann et al. 2006). In more astringent climates, such as warm and humid regions omitting some coastal region, research has shown that pristinely natural ventilation systems are not sufficient to sustain acceptable thermal comfort (Emmerich 2006, Zhai, Johnson et al. 2011). A study on the integration of natural ventilation into the HVAC systems operation of indoor thermal control showed an achievement of a reduction in energy consumptions in warmer climates, such as humid sub- tropical (Axley 2001), tropical (Haase and Amato 2009), and arid (Ezzeldin, Rees, et al. 2009).
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Coupling between Geothermal Energy and Solar Energy for Creation of New Energetic System “Production of Electricity and Hydrogen” in Intelligent Buildings

Coupling between Geothermal Energy and Solar Energy for Creation of New Energetic System “Production of Electricity and Hydrogen” in Intelligent Buildings

Production of electricity by coupling between many kinds of renewable energy was developed in order to increase the amount of electrical power, for using it in intelligent buildings and distributed it in the grid at the same time (produced amount is more than demand amount). The obtained results will inject it for production of hydrogen at different temperature, because the temperature of vapor water arrive until 1726°C. The amount of electrical power produced by coupling between many sources of renewable energy is more than the amount using only one source. The second model (small generator) can give more amount of energy than the other models. Finally; the next research will be the application of obtained results of the second model (small generator) for production of hydrogen from different sources (at different temperature).
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Development of an intelligent decision supporting
home energy management system

Development of an intelligent decision supporting home energy management system

The Data processing model will provide the all collected information including customer preferences, environmental condition, room temperature, grid prices and available renewable energy to the Decision making system. Then the Decision making system will evaluate the total consumptions according to the Cost reduction objective and Comfort life style. An example is when the system will receive any signals from Smart meter about load shedding or Direct Load control, and it will match with customer requirements. If customer chooses the cost reduction option and there are no priority loads then the Decision making system will participate in Direct Load Control (DLC) program to shift their demands through changing on and off circle based on the real time prices for saving energy cost. Here customers can adjust their consumption through setting the operating time of some of the home appliances as an example Washing machine, dishwasher etc. The home electricity applicants can be categorized into three types:
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Energy Efficient Management and Optimization Strategies in Office Buildings

Energy Efficient Management and Optimization Strategies in Office Buildings

Analyzing the results helped in observing that for the first scenario, the power consumption from fans and the energy required to heat the secondary coils was relatively higher than the actual case. This was a result of using the maximum flow rate of 18000m³/hr for 1.20 hours more than the actual case by 20 minutes to heat up the zone to satisfy the set temperature. However, by neglecting the radiator system and decreasing the supply temperature from 31 C˚, in actual case, to 26 C˚ and by using less flow rate during the day (9000 m³/hr) the savings reached 13%. For the second scenario, the radiator system consumed more energy than the actual case, since it was scheduled to work at 5:00 am earlier than the actual case by 20 minutes. While the power consumption from the fan decreased, since it started later 15 minutes than the actual case with less flow rate. Not only the scheduling have affect the consumption from the ventilation system, but also the supply air temperature was lessen to 26 C˚ which influenced the consumption from heating the secondary coil. This scenario was successful to reach savings of 24%. The optimized scenario does not have much difference in terms of savings when compared to the second scenario. Thus, it was successful in finding the optimum values for the control parameters.
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An Epistemic-Deontic-Axiologic (EDA) agent-based Energy Management System in office buildings

An Epistemic-Deontic-Axiologic (EDA) agent-based Energy Management System in office buildings

personal agents has the function of representing occupants; the local agent has the function of control the environment parameters and the central agent has the function such as con figuring the whole system. Wu and Noy [23] suggested a multi-agent based system to reconcile the occupants’ well-being and energy consumption in domestic buildings. The proposed prototype system model is integrated with a wireless sensor actuator network (WSAN) to collect environmental information. Personal agents are recognised playing an important role in helping the system to ful fil individual requirements. Rogers et al. [24] proposed a home energy management agent to optimise the use of the heating system on behalf of the householder. The agent considers the comfort, carbon emissions and cost of energy to make control decisions. Feed- backs from the system sent to the occupants contains the cost and carbon emission information. Yang and Wang [25] developed a multi- agent system for building energy management also including the per- sonal agents, the local agents and the central agents. The agents are arranged in a hierarchical way with multiple local agents connected to one central agent while a local agent is serving more than one personal agent. The current multi-agent-based energy management model are suggested to be grouped into different levels such as master agents and slave agents [26] . The master agents respond to energy efficiency and
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Wsn Based Smart Sensor and Actuator for Power Management in Intelligent Buildings

Wsn Based Smart Sensor and Actuator for Power Management in Intelligent Buildings

Where , Pcal is the calculated power; Vact the output voltage as given in (1); Iact the current value as given in (2); and Cf is the correction factor. The term correction factor is introduced to calculate power accurately by the system. The correction factor is the ratio of actual power to the measured power. Correction factor is required for the power measurement for some loads. This correction factor can be obtained by plotting graph for calculated power against the actual power. Thus, the power is calculated in computer using CSharp programming after receiving voltage outputs from corresponding current and voltage sensors. The prototype has been tested and results achieved for many household electrical appliances are shown in the following section.
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Design of Cloud-Connected IoT System for Smart Buildings on Energy Management (Invited paper)

Design of Cloud-Connected IoT System for Smart Buildings on Energy Management (Invited paper)

In particular, it is important to have low cost BEM solutions that can immediately detect the energy wastage from an office building and be flexible enough to install into retrofit buildings. In this regard, a number of recent studies investigate the issue of cost- effective energy management in office spaces. For instance, a simulation based study of an office building is conducted in [4] to investigate the uncertainties in energy consumption due to actual weather condition and the building’s operational practices. To manage the energy for electrical, heating, and cooling energy zones of a building, a semi-centralized decision making process using multi-agent systems is proposed in [5] for joint heat and power optimization with a view to improve energy efficiency and reduce energy cost.
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Development of an intelligent decision supporting

home energy management system

Development of an intelligent decision supporting home energy management system

The developed system is referred as Intelligent Decision support system (IDSS) because it will use consumer s’ comfort or preferences to control their energy consumptions and take intelligent decision on behalf of the consumers to meet various demands and respond to several factors. A Fuzzy Multi Criteria Decision Making (MCDM) tool has been used to quantify and evaluate cons u ers’ comfort level according to peak and off hours in order to best manage the use of the appliances. The purpose of using Fuzzy Multi Criteria Decision Making (MCDM) system is because it can solve decision and planning problems involving multiple criteria. As an example, if someone wants to purchase a car, cost, comfort, safety, and fuel economy may be the main criteria to consider. The Fuzzy MCDM will consider different variables as inputs and calculate the outputs in according to the inputs change. In this proposed work the inputs for the Fuzzy MCDM load controller are Time (peak and off peak hours), consumer comfort level temperature, temperature deviation, forecast load and consumption time. The outputs from the Fuzzy MCDM load controller include: Allow load scheduling and Run loads.
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An Intelligent Control Strategy of Battery Energy Storage System for Microgrid Energy Management under Forecast Uncertainties

An Intelligent Control Strategy of Battery Energy Storage System for Microgrid Energy Management under Forecast Uncertainties

Several conclusions could be deduced from observing Fig. 7 (the left). Firstly, energy storage (BESS) can reduce the cost of microgrid in purchasing electricity energy, as depicted with blue line and other lines. Because the strategy of load following and normal operation with no BESS are both rule based real-time operation strategy, the cost of microgrid in these strategies have no difference with forecast error level, so they are two parallel curves. Secondly, BESS could play an more important role in microgrid operation if suitable strategies or methods are used, as shown in Fig. 7 (the left), there is a big difference of microgrid cost between load following strategy and day-ahead programming and
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Economic Power Schedule and Transactive Energy through Intelligent Centralized Energy Management System for DC Residential Distribution System

Economic Power Schedule and Transactive Energy through Intelligent Centralized Energy Management System for DC Residential Distribution System

A hierarchical approach could adopt for designing for the control system of DC residential system, which is identified: primary, secondary, and tertiary control. Converters control is based on the voltage droop control to share power for DGs and be responsible for tracking DC voltage reference. The secondary control is for removing voltage deviation and reliable operation, the tertiary control is responsible for the economical and coordinated operation and host grid that related to transactive energy control. In this work, we mainly considerate the primary control. PV and WT are preferred to inject maximum power and operated in MTTP mode, however, the output voltages of DERs in common bus should be the priority. The bidirectional and directional converter are mandatory to adaptive the out voltages through adaptive droop loop. The equivalent circuit of voltage droop control for three parallel voltage source converters is shown in Fig. 2.
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Hierarchical Energy Management System for Smart Buildings

Hierarchical Energy Management System for Smart Buildings

energy source. For the real time security a virtual home is implemented. Remote user can access the system using the Internet. They are then wirelessly transmitted to the Home Gateway using the homes Wi-Fi network. A ZigBee based remote control can be used to directly control connected devices and integrated with the home device database which contain status of all the connected device. Once the communication are sent to the real home automation system the communications are checked for security. All the devices connected with the Zigbee Network are allotted with a dedicated controller. The sensitive data from the controller and encrypted with the valid key for security. The data are decrypted at the receiving end and checked for authentication. The device address is extracted from the message and the device is checked for existence and status of the device is verified.
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Design and Implementation of Intelligent Energy Distribution Management System

Design and Implementation of Intelligent Energy Distribution Management System

Many industrial and industrial firms still haven't administered energyaudits to gather reliable info on their current operations. Whereas this mightbe due to a failure by management to understand the potential advantages of energy efficiency, some firms can lack skilled personnel able to perform audits. Consideration ought to lean to mistreatment outside specialists, because the price can commonly be well even. The demand response coordination downside is developed as a bi-level optimization downside wherever the higher sub problem seeks to switch system load profile and also the lower sub problems minimize individual customers’ energy expenses and ensure their comfort. The bi-level model is then relaxed to a single-level improvement problem thatis way less complicated to solve. Most DSM measures
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Intelligent energy management system - techniques and methods

Intelligent energy management system - techniques and methods

A key feature of Smart Meter technology is the ability to reduce peak load on the very hottest days by providing financial incentives to customers who voluntarily shift electricity usage away from critical peaks, which in the case of California will reduce PG&Es need to purchase power to meet demand at the most critical times, help avoid strain on the power grid, and help reduce reliance on fossil-fuel generation. To achieve those benefits, the CPUC (California Public Utilities Commission) approved PG&E’s proposal to provide customers with a Critical Peak Pricing choice. The Smart Meter device is almost identical in size and appearance to the existing electric meter. The gas smart meter module is a tiny part which can be installed in the existing gas meter. Many companies in the USA are ready to employ smart meters. However, PG&E’s program is the largest one in the United States. PG&E’s investment to develop the new smart meter technology is estimated to be $1.74 billion. PG&E projects that these investments will be offset through the energy saving achieved by using the new smart meters [22].
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Intelligent Energy Management for Hybrid System Based On Microgrid

Intelligent Energy Management for Hybrid System Based On Microgrid

Abstract: A microgrid can be operated in on-grid or off-grid, mode using distributed energy resources (DER), among which combined heat power can play an important role in increasing the total energy efficiency. This paper reports the total solutions to the problem of scheduling DER including CHP and their dispatch, called a generation application package. To increase the accuracy of forecasting, this paper proposes the methods of electrical or heat load forecasting and photovoltaic forecasting using a pattern recognition algorithm by k-means clustering. In response of the forecasted data, the formulation and solution of generation scheduling and dispatch of DER are provided in two operation modes of a microgrid. The formulation and the detailed solution of the economic dispatch of the DER are presented. Finally, field tests of the generation applications package were performed and the results shown of the the proposed solutions are very effective in the real fields. This paper proposes the dynamic optimal schedule management method for an isolated or grid-connected microgrid system with renewable power generations such as PV systems and wind power systems, and with back-up and storage sources such as micro-turbine generators and electricity storage, which is able to consider forecast errors with uncertainties of solar radiation, wind speed and local user demands. The energy management system (EMS) structure for accumulation interms of the economic and stable operation in the microgrid is proposed. Optimization problem for the power from micro-turbine generators or the grid and electricity storage in EMS is resolved by dynamic programming (DP) and equal incremental fuel cost method. The proposed method is confirmed by matlab study that used forecast data and realization data of renewable power generations and local user demands. In conclusion, this paper presented a practicable method for the operation of a microgrid.
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The intelligent system for smart buildings

The intelligent system for smart buildings

The demands made on modern electrical installations in private homes and on business premises have changed considerably. More and more emphasis is being placed on safety, operational ease and flexible use. The limits for conventional installations with a confusing number of own functional networks for electrical power, heating, lighting and shutter control, burglar alarm system, smoke, gas and fire detectors, however, have long been reached. Installation and power costs have increased. Subsequent upgrading, renovation and change of system operation is expensive and complicated. The KNX System offers a convincing perspective. The KNX System is an intelligent building management system for measuring, regulating, switching, controlling, signalling and monitoring.
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Energy Efficient Lighting System in Gaza Strip Buildings

Energy Efficient Lighting System in Gaza Strip Buildings

Irada building hosts more than one hundred craftsmen; in addition to, some College of Commerce staff and trainers. The working hours are between 1600 to 1800 hours per year for an average of 6-8 hours daily with five working days weekly. The building consists of three floors: basement, ground floor and the first floor. All workshops are in the basement floor, all management offices and laboratory are in ground floor while showrooms are in first floor. Table 1 presents the annual electrical consumption of the lighting system in all floors of Irada building.
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