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نتیجه جستجو - Electricity consumption

تعداد مقالات یافته شده: 22
ردیف عنوان نوع
1 Federated learning with hyperparameter-based clustering for electrical load forecasting
یادگیری فدرال با خوشه‌بندی مبتنی بر فراپارامتر برای پیش‌بینی بار الکتریکی-2022
Electrical load prediction has become an integral part of power system operation. Deep learning models have found popularity for this purpose. However, to achieve a desired prediction accuracy, they require huge amounts of data for training. Sharing electricity consumption data of individual households for load prediction may compromise user privacy and can be expensive in terms of communication resources. Therefore, edge computing methods, such as federated learning, are gaining more importance for this purpose. These methods can take advantage of the data without centrally storing it. This paper evaluates the performance of federated learning for short-term forecasting of individual house loads as well as the aggregate load. It discusses the advantages and disadvantages of this method by comparing it to centralized and local learning schemes. Moreover, a new client clustering method is proposed to reduce the convergence time of federated learning. The results show that federated learning has a good performance with a minimum root mean squared error (RMSE) of 0.117 kWh for individual load forecasting.
Keywords: Federated learning | Electricity load forecasting | Edge computing | LSTM | Decentralized learning
مقاله انگلیسی
2 Multi-objective optimization modelling of sustainable green supply chain in inventory and production management
مدلسازی چند هدفه بهینه سازی زنجیره تأمین سبز پایدار در مدیریت موجودی و تولید-2021
The ever increasing pressure to conserve the environment from global warming cannot be overemphasized. Emission from the inventory and production process contributes immensely to global warming and hence, the need to device a sustainable green inventory by the operational managers. In this paper, a multi-item multi-objective inventory model with back-ordered quantity incorporating green investment in order to save the environment is proposed. The model is formulated as a multi-objective fractional programming problem with four objectives: maximizing profit ratio to total back-ordered quantity, minimizing the holding cost in the system, minimizing total waste produced by the inventory system per cycle and minimizing the total penalty cost due to green investment. The constraints are included with budget limitation, space restrictions, a constraint on cost of ordering each item, environmental waste disposal restriction, cost of pollution control, electricity consumption cost during production and cost of greenhouse gas emission in the production process. The model effectiveness is illustrated numerically, and the solution obtained to give a useful suggestion to the decision-markers in the manufacturing sectors.
KEYWORDS: Multi-objective fractional programming | Fuzzy goal programming | Sustainable green supply chain | Inventory and production management
مقاله انگلیسی
3 Smart charging of electric vehicles considering photovoltaic power production and electricity consumption: A review
شارژ هوشمند وسایل نقلیه برقی با توجه به تولید انرژی فتوولتائیک و مصرف برق: بررسی-2020
Photovoltaics (PV) and electric vehicles (EVs) are two emerging technologies often considered as cornerstones in the energy and transportation systems of future sustainable cities. They both have to be integrated into the power systems and be operated together with already existing loads and generators and, often, into buildings, where they potentially impact the overall energy performance of the buildings. Thus, a high penetration of both PV and EVs poses new challenges. Understanding of the synergies between PV, EVs and existing electricity consumption is therefore required. Recent research has shown that smart charging of EVs could improve the synergy between PV, EVs and electricity consumption, leading to both technical and economic advantages. Considering the growing interest in this field, this review paper summarizes state-of-the-art studies of smart charging considering PV power production and electricity consumption. The main aspects of smart charging reviewed are objectives, configurations, algorithms and mathematical models. Various charging objectives, such as increasing PV utilization and reducing peak loads and charging cost, are reviewed in this paper. The different charging control configurations, i.e., centralized and distributed, along with various spatial configurations, e.g., houses and workplaces, are also discussed. After that, the commonly employed optimization techniques and rulebased algorithms for smart charging are reviewed. Further research should focus on finding optimal trade-offs between simplicity and performance of smart charging schemes in terms of control configuration, charging algorithms, as well as the inclusion of PV power and load forecast in order to make the schemes suitable for practical implementations.
Keywords: Photovoltaics | Electric vehicles | Electricity consumption | Smart charging | Energy management system | Charging optimization
مقاله انگلیسی
4 Energy management for cost minimization in green heterogeneous networks
مدیریت انرژی برای به حداقل رساندن هزینه در شبکه های ناهمگن سبز-2020
With the fast development of cyber–physical systems, mobile applications and traffic demands have been significantly increasing in this decade. Likewise, the concerns about the electricity consumption impacts on environments and the energy costs of wireless networks have also been growing greatly. In this paper, we study the problem of energy cost minimization in a heterogeneous networks with hybrid energy supplies, where the network architecture consists of radio access part and wireless backhaul links. Owing to the diversities of mobile traffic and renewable energy, the energy cost minimization problem involves both temporal and spatial dimensional optimization. We decompose the whole problem into four subproblems and correspondingly our proposed solution consists of four parts: At first, we obtain estimated average energy consumption profiles for all base stations based on the temporal traffic statistics; Second, we formulate the green energy allocation optimization in the temporal domain to minimize energy cost for each BS. Third, given the allocated green energy and practical user distribution in each slot, we propose a centralized and a distributed user association algorithm to minimize total energy cost in the spatial dimension. Fourth, based on the actual user association scheme, we readjust the green energy allocation for each BS to further improve green energy utilization. Simulation results show that our proposed solution can significantly reduce the total energy cost, compared with the recent peer algorithms.
Keywords: Energy cost minimization | Green energy | Heterogeneous networks | Cyber–physical systems
مقاله انگلیسی
5 A multi-stage stochastic energy management of responsive irrigation pumps in dynamic electricity markets
مدیریت انرژی تصادفی چند مرحله ای از پمپ های آبیاری پاسخگو در بازارهای برق پویا-2020
The penetration of renewable resources, such as wind and solar energies, is increasing all over the world. Against the conventional thermal power systems, the intermittency and volatility of renewable energies are formidable challenges to the future power system operation. Therefore, future power systems need alternative forms of flexibility to hedge against intermittent power. Demand-side flexibility is a practical solution that attracted much attention in recent years. There is structural flexibility in electricity consumption, including residential, commercial, agricultural and industrial sectors, which can be integrated into future power systems. Against the literature on residential, commercial, and industrial sectors, agricultural flexibility is still a challenge in power systems. This paper aims to narrow this gap by proposing a responsive structure for agricultural irrigation systems. Achieve this aim, first of all, a mathematical model is proposed for responsive irrigation systems considering groundwater, surface and booster water pumps. After that, a multi-stage stochastic approach is addressed to schedule a time-oriented agricultural demand response program from 24 h ahead to near real-time. Then, a multi-layer structure is suggested to integrate the farm flexibility from the demand-side, i.e. responsive farms, into the supply-side, i.e. the dynamic electricity market, through a devised agricultural demand response aggregator. Finally, the approach is implemented in the Danish sector of the Nordic Electricity Market to show the applicability of the proposed framework. The results show that the proposed responsive irrigation system can provide great flexibility to power systems in comparison with traditional irrigation systems.
Keywords: Electricity market | Farm | Irrigation system | Renewable energy | Stochastic programming
مقاله انگلیسی
6 AI-Powered Home Electrical Appliances as Enabler of Demand-Side Flexibility
لوازم برقی خانگی با استفاده از هوش مصنوعی به عنوان امکان انعطاف پذیری سمت تقاضا-2020
In the digitalization era, the increasing number of connected appliances and the rise of artificial intelligence (AI) enabled a new realm of possibilities in the residential energy sector, including the chance for a consumer to play an active role in flexibility programs. We talk about demand-side flexibility (DSF) when a consumer adapts his/her energy consumption behavior in response to variable energy prices or market incentives. The procedure depends on a two-way communication between an energy supplier and a customer, and his/her willingness to act on the electricity consumption. The success of the different DSF approaches is strongly related to the estimation of appliance usage patterns and AI techniques represent a viable solution.
مقاله انگلیسی
7 Consumers’ sensitivities and preferences modelling and integration in a decentralised two levels energy supervisor
حساسیت ها و ترجیحات مصرف کنندگان مدل سازی و ادغام در یک ناظر غیر متمرکز انرژی در دو سطح-2020
To address the new challenges arising from the higher penetration of renewable energy in electrical grid, Demand Response (DR) aims to involve the residential consumers in the grid equilibrium. Ensuring benefits for both utility and users requires the consumers sensitivities to be understood and then included in the Energy Management System (EMS). For this purpose, the cost is the predominant and most often only factor taken into account in the literature, although in the residential sector other concerns influencing electricity consumption behaviour have been observed. This paper presents a two levels EMS applied to a neighbourhood of consumers mathematically modelled at the level of their appliances and incorporating 5 consumers profiles along three sensitivities: cost, environment and appliances shifting comfort. The first level is a day ahead supervision based on a multi-agent optimisation lead by a central aggregator but performed locally by the household using Dynamic Programming (DP), thus ensuring privacy protection for the stakeholders. The second level is a real time supervision using the same decentralised structure and based on fuzzy logic. Both levels are evaluated in this paper, with a focus on the balance between grid and consumers objectives.
Keywords: Demand response | Energy management | Game Theory | Fuzzy Logic | Decentralised load management | Consumers profiles
مقاله انگلیسی
8 A pattern recognition methodology for analyzing residential customers load data and targeting demand response applications
یک روش تشخیص الگو برای تجزیه و تحلیل داده های باربری مشتریان مسکونی و هدف قرار دادن برنامه های پاسخ تقاضا-2019
The availability of smart meter data allows defining innovative applications such as demand response (DR) programs for households. However, the dimensionality of data imposes challenges for the data min- ing of load patterns. In addition, the inherent variability of residential consumption patterns is a major problem for deciding on the characteristic consumption patterns and implementing proper DR settle- ments. In this regard, this paper utilizes a data size reduction and clustering methodology to analyze residential consumption behavior. Firstly, the distinctive time periods of household activity during the day are identified. Then, using these time periods, a modified symbolic aggregate approximation (SAX) technique is utilized to transform the load patterns into symbolic representations. In the next step, by applying a clustering method, the major consumption patterns are extracted and analyzed. Finally, the customers are ranked based on their stability over time. The proposed approach is applied on a large dataset of residential customers’ smart meter data and can achieve three main goals: 1) it reduces the dimensionality of data by utilizing the data size reduction, 2) it alleviates the problems associated with the clustering of residential customers, 3) its results are in accordance with the needs of systems oper- ators or demand response aggregators and can be used for demand response targeting. The paper also provides a thorough analysis of different aspects of residential electricity consumption and various ap- proaches to the clustering of households which can inform industry and research activity to optimize smart meter operational use
Keywords: Clustering algorithms | Demand response | Load patterns | Smart meters | Symbolic aggregate approximation (SAX)
مقاله انگلیسی
9 A shape-based clustering method for pattern recognition of residential electricity consumption
یک روش خوشه بندی مبتنی بر شکل برای تشخیص الگوی مصرف برق مسکونی-2019
Pattern recognition of residential electricity consumption refers to discover different electricity consumption patterns from electricity consumption data (ECD), which can provide valuable insights for developing personalized marketing strategies, supporting targeted demand side management, and improving energy utilization efficiency. To improve the efficiency and effectiveness of ECD analysis, we proposed an improved K-means algorithm, in which principal component analysis (PCA) was used to reduce the dimensions of smart meter time series data and the initial cluster centers were optimized. 3000 daily electricity consumption profiles (ECPs) of 1000 residents, obtained from the smart metering electricity customer behavior trials of Irish, and 2000 yearly residential ECPs from Jiangsu Province, China, were used in the experiments. The ECPs were divided into 7 and 4 clusters respectively based on their ECPs, and the characteristics of each cluster were extracted. In addition, the changes of residential electricity consumption are also reflected in the shape variation of ECPs. However, traditional similarity measurements cannot find the shape similarity of ECPs. Therefore, a shape-based clustering method was also proposed to group ECPs with similar shapes and the detailed algorithm procedures were provided. The results showed that the shape-based clustering method can effectively find similar shapes and identify typical electricity consumption patterns based on daily ECPs
Keywords: Electricity consumption pattern | Shape-based clustering | Dynamic time wrapping | Smart meter data
مقاله انگلیسی
10 A personalized electricity tariff recommender system based on advanced metering infrastructure and collaborative filtering
یک سیستم توصیه گر تعرفه برق شخصی مبتنی بر زیرساخت های اندازه گیری پیشرفته و فیلترهای مشترک-2019
Deregulation of electricity retail markets and the advancement of energy informatics have been supporting the transition of electricity retail business into an electronic business, where different electricity retailers can provide different electricity tariff plans to end users through digital media. In this context, end users are facing an information filtering challenge of choosing the most suitable tariff plans from a set of candidate tariff plans. This paper proposes a new personalized recommendation system that makes intelligent electricity tariff recommendations to end users. The proposed approach starts by collecting a group of end users’ electricity consumption profiles through the advanced metering infrastructure and, based on this information, it infers the preference of individual users on each tariff plan. Based on the inferred preference degree, a new matrix factorization is established based on a collaborative filtering algorithm that is capable of recommending most suitable tariff plans to an arbitrary target user. The proposed recommendation system is validated against a number of scenarios that are generated based on simulated tariff plan sets and on a modified Australian “Smart Grid, Smart City” dataset.
Keywords: Collaborative filtering | Power market | Recommendation system | Demand side management | Smart grid
مقاله انگلیسی
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