Classification of Regions by Climatic Characteristics for the Use of Renewable Energy Sources

Economic development, technological innovation, and policy change are especially prominent factors shaping energy transitions. The current stage of the energy transition is distinguished by a qualitatively new level of policy influence on all components of the energy complex. This policy affects the motivation and behavior of all stakeholders in the processes of generation, distribution, and use of energy. In terms of their importance, behavioral aspects have reached the same level as technology and energy economics. In the article, we analyze these features of the energy complex and offer modified and refined models for the innovative development of the energy complex as a socio-technical system. The proposed approach, in which the energy complex is considered a socio-technical system, can be taken into account when describing the sectoral innovation system in the energy industry. Considering the ongoing trends in digitalization and the advancement of cyber-physical systems, the general principles outlined by the authors for homogeneous production systems can be applied effectively in the management of projects related to the development of energy complex subsystems.

Section: Resultsmentioning

confidence: 99%

Innovation Management Models in the Energy Sector

Nurulin,

Skvortsova,

Konovalova

2023

“…There is a note that issues related to sustainable development (SD) have become relevant in the last few decades. The idea of sustainable development corresponds to the global nature of society's problems, and many states and their constituents use it to develop effective management strategies for socio-economic systems (Gutman et al, 2022;Brazovskaia and Gutman 2021).…”

Section: Introductionmentioning

confidence: 99%

Integral Indicator Assessment of Municipalities Sustainability in the Leningrad Region

Tanina,

Orel,

Zaborovskaia

et al. 2023

Sustainable development has become a prominent reference point in strategic planning and territorial improvement. Economic growth necessitates intensified efforts to utilize resources, often resulting in increased pressure on the environment and heightened social inequality. The application of sustainable development principles holds particular importance for urbanized territories. Assessing the regional sustainability integral indicator can help alleviate unequal socioeconomic development among municipalities. This indicator comprises indexes of the sustainability of individual territories. The authors propose the integral indicator as the arithmetic mean of indexes reflecting the sustainable development level of each component (economic, social, environmental). The authors applied this tool to municipalities in the Leningrad Region. Additionally, they conducted a ranking of municipal districts in the region based on the integral indicator. The leaders in sustainable development were the districts included in the St. Petersburg agglomeration. The authors suggest that a significant factor in the sustainable development of a territory is the presence of small enterprises, which possess the necessary flexibility for innovation in the social and environmental spheres. The authors propose distributing the elements of small and medium-sized enterprises (SME) potential according to the sustainable development factors of the region. The obtained data will enable the making of administrative decisions at the municipal and regional levels, including those related to the intensity and support for SMEs operating in relevant industries. This methodological approach to assessing the sustainability of the region and its internal municipalities, particularly concerning SMEs, can be utilized to make optimal administrative decisions related to government support for specific business areas.

“…Any excessive use of electricity should be avoided. Moreover, increasing the energy efficiency of existing energy systems and lowering harmful emissions into the atmosphere is necessary to transition from fossil fuels to "green" energy (Brazovskaia & Gutman, 2021).…”

Section: Introductionmentioning

confidence: 99%

Anomaly Prediction in Electricity Consumption Using a Combination of Machine Learning Techniques

Elhadad¹,

Tan²,

Tan³

2022

Electricity demand is increasing proportionally to the increase in power usage. Without a doubt, energy efficiency has gained significant importance and attention, with one of the primary concerns being the detection and forecasting of abnormal consumption. In this paper, the authors proposed a method to predict the occurrence of abnormal consumption behavior in advance. The proposed method utilizes the Isolation Forest algorithm to label the smart meter electricity consumption readings as normal or abnormal. It generates a sequence of data with varying lengths. Based on the data sequence, two supervised machine learning algorithms, Random Forest, and Decision Tree were developed to forecast the occurrence of power anomaly consumption. Experiment results showed that the proposed methods consistently detect and predict the abnormal status 30 minutes ahead. There is no significant difference between Random Forest and Decision Tree performance on different smart meter readings, dataset sizes, and other data sequence lengths. The proposed methods portray an alternative approach that is capable of autolabel normal and abnormal data and, as a result, dealing with the sequence of label data in the prediction process while avoiding the dynamic behavior of the power consumption data.