Energy Efficiency Improvement of Industrial Enterprise Based on Machine Learning Electricity Tariff Forecasting

Matrenin, Pavel; Antonenkov, Dmitriy; Arestova, Anna

doi:10.1109/apeie52976.2021.9647491

Cited by 5 publications

(4 citation statements)

References 10 publications

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“…Advanced Metering Infrastructure (AMI) is the main requirement to activate wide-spread smart meter usage. There have been many efforts exerted in the development of smart meters, such as using artificial intelligence and data clustering for the full supervision of the customers' energy consumption [102,103].…”

Section: Imentioning

confidence: 99%

“…Additionally, various search methods and objective functions, along with trade-offs in GA and simulation parameter settings, are examined. In [103], the researchers introduced three different patterns for various building types in Norway, intending to determine the model that provides the best incentive control for the demand side in buildings. The algorithm is used to calculate different tariffs total pricing of the buildings' heat load shift and the savings per kWh of relocated load.…”

Section: Electricity Tariffs Research Developmentsmentioning

confidence: 99%

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A Review of Electricity Tariffs and Enabling Solutions for Optimal Energy Management

Zaki

Hamdy

2022

Energies

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Today, electricity tariffs play an essential role in the electricity retail market as they are the key factor for the decision-making of end-users. Additionally, tariffs are necessary for increasing competition in the electricity market. They have a great impact on load energy management. Moreover, tariffs are not taken as a fixed approach to expense calculations only but are influenced by many other factors, such as electricity generation, transmission, distribution costs, and governmental taxation. Thus, electricity pricing differs significantly between countries or between regions within a country. Improper tariff calculation methodologies in some areas have led to high-power losses, unnecessary investments, increased operational expenses, and environmental pollution due to the non-use of available sustainable energy resources. Due to the importance of electricity tariffs, the authors of this paper have been inspired to review all electricity tariff designs used worldwide. In this paper, 103 references from the last ten years are reviewed, showing a detailed comparison between different tariff designs and demonstrating their main advantages and drawbacks. Additionally, this paper reviews the utilized electricity tariffs in different countries, focusing on one of the most important countries in the Middle East and North Africa regions (Egypt). Finally, some recommended solutions based upon the carried-out research are discussed and applied to the case study for electricity tariff improvement in this region. This review paper can help researchers become aware of all the electricity tariff designs used in various countries, which can lead to their design improvements by using suitable software technologies. Additionally, it will increase end-users’ awareness in terms of deciding on the best electricity retail markets as well as optimizing their energy usage.

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Section: Imentioning

confidence: 99%

Section: Electricity Tariffs Research Developmentsmentioning

confidence: 99%

A Review of Electricity Tariffs and Enabling Solutions for Optimal Energy Management

Zaki

Hamdy

2022

Energies

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“…The complexity of such accounting limits the scope of application of these methods by individual enterprises, while regulatory methods can be applied to relatively large territorial units (network nodes and energy districts). Difficulties in predicting specific indicators of electricity consumption constrain the use of both of the above methods [7].…”

mentioning

confidence: 99%

“…It is known that there are a large number of variables that affect the mode of power consumption and, accordingly, the accuracy of its forecast. These variables differ a lot and can be divided into explicit and implicit (latent), exogenous (originated outside the power system) and endogenous (conversely, born by the EPS itself) [7]. Power consumption, frequency, power losses, and overflows are clearly endogenous variables.…”

mentioning

confidence: 99%

A Rank Analysis and Ensemble Machine Learning Model for Load Forecasting in the Nodes of the Central Mongolian Power System

Osgonbaatar,

Matrenin,

Safaraliev

et al. 2023

Inventions

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Forecasting electricity consumption is currently one of the most important scientific and practical tasks in the field of electric power industry. The early retrieval of data on expected load profiles makes it possible to choose the optimal operating mode of the system. The resultant forecast accuracy significantly affects the performance of the entire electrical complex and the operating conditions of the electricity market. This can be achieved through using a model of total electricity consumption designed with an acceptable margin of error. This paper proposes a new method for predicting power consumption in all nodes of the power system through the determination of rank coefficients calculated directly for the corresponding voltage level, including node substations, power supply zones, and other parts of the power system. The forecast of the daily load schedule and the construction of a power consumption model was based on the example of nodes in the central power system in Mongolia. An ensemble of decision trees was applied to construct a daily load schedule and rank coefficients were used to simulate consumption in the nodes. Initial data were obtained from daily load schedules, meteorological factors, and calendar features of the central power system, which accounts for the majority of energy consumption and generation in Mongolia. The study period was 2019–2021. The daily load schedules of the power system were constructed using machine learning with a probability of 1.25%. The proposed rank analysis for power system zones increases the forecasting accuracy for each zone and can improve the quality of management and create more favorable conditions for the development of distributed generation.

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Adapting to Changes: A Novel Framework for Continual Machine Learning in Industrial Applications

Antony,

Jalušić,

Bergweiler

et al. 2024

J Grid Computing

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Energy Efficiency Improvement of Industrial Enterprise Based on Machine Learning Electricity Tariff Forecasting

Cited by 5 publications

References 10 publications

A Review of Electricity Tariffs and Enabling Solutions for Optimal Energy Management

A Review of Electricity Tariffs and Enabling Solutions for Optimal Energy Management

A Rank Analysis and Ensemble Machine Learning Model for Load Forecasting in the Nodes of the Central Mongolian Power System

Adapting to Changes: A Novel Framework for Continual Machine Learning in Industrial Applications

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