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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 |