scholarly journals Intelligent Predictive Analytics for Sustainable Business Investment in Renewable Energy Sources

2020 ◽  
Vol 12 (7) ◽  
pp. 2817 ◽  
Author(s):  
Theodoros Anagnostopoulos ◽  
Grigorios L. Kyriakopoulos ◽  
Stamatios Ntanos ◽  
Eleni Gkika ◽  
Sofia Asonitou
Keyword(s):  
Renewable Energy ◽  
Bayesian Network ◽  
Short Term Memory ◽  
Predictive Analytics ◽  
Renewable Energy Sources ◽  
Energy Sources ◽  
Support Vector ◽  
Sustainable Business ◽  
K Nearest Neighbors ◽  
Bayesian Network Classifier

Willingness to invest in renewable energy sources (RES) is predictable under data mining classification methods. Data was collected from the area of Evia in Greece via a questionnaire survey by using a sample of 360 respondents. The questions focused on the respondents’ perceptions and offered benefits for wind energy, solar photovoltaics (PVs), small hydro parks and biomass investments. The classification algorithms of Bayesian Network classifier, Logistic Regression, Support Vector Machine (SVM), C4.5, k-Nearest Neighbors (k-NN) and Long Short Term Memory (LSTM) were used. The Bayesian Network classifier was the best method, with a prediction accuracy of 0.7942. The most important variables for the prediction of willingness to invest were the level of information, the level of acceptance and the contribution to sustainable development. Future studies should include data on state incentives and their impact on willingness to invest.

scholarly journals The Circular Economy at Enel X

2020 ◽  
pp. 101
Author(s):  
Nicola Tagliafierro
Keyword(s):  
Renewable Energy ◽  
Value Chain ◽  
Circular Economy ◽  
Paradigm Shift ◽  
Business Models ◽  
Renewable Energy Sources ◽  
Life Cycles ◽  
Energy Sources ◽  
Product Portfolio ◽  
Sustainable Business

Enel X is leading the transition toward a sustainable business model, with the circular economy as an important pillar. Using renewable energy sources and materials, extending product life cycles, creating sharing platforms, reuse and regeneration, rethinking products as services. The principles of the circular economy have become essential, considering the paradigm shift overturning the traditional linear economic model. Enel X was one of the first businesses to offer products on the market that concretely apply the five business models of the circular economy and reconsider the entire value chain from a sustainability perspective. This approach is characterized by two core principles: 1.  the first, addressed internally, focuses on the business’s product portfolio, which ranges from “measuring” circularity to identifying solutions for improvement; 2.  the second is directed toward the outside, and especially toward industrial customers and public administrations or end customers, with the goal of evaluating their level of “circularity” and helping them outline a roadmap to circularity.

scholarly journals Application of the Least Square Support Vector Machine for point-to-point forecasting of the PV Power

2019 ◽  
Vol 9 (4) ◽  
pp. 2205
Author(s):  
Mahdi Farhadi ◽  
Nader Mollayi
Keyword(s):  
Support Vector Machine ◽  
Renewable Energy ◽  
Numerical Range ◽  
Renewable Energy Sources ◽  
Energy Resource ◽  
Least Square ◽  
Energy Sources ◽  
Support Vector ◽  
Solar Radiation Intensity ◽  
Point To Point

<p>In today's industrial world, the growing capacity of renewable energy sources is a crucial factor for sustainable power generation. The application of solar photovoltaic (PV) energy sources, as a clean and safe renewable energy resource has found great attention among the consumers in the recent decades. Accurate forecasting of the generated PV power is an important task for scheduling the generators and planning the consumption patterns of customers to save electricity costs. To this end, it is necessary to develop a global model of the generated power based on the effective factors which are mainly the solar radiation intensity and the ambient weather temperature. As a result of the wide numerical range of these parameters and various weather conditions, a large training database must be used for developing the models, which results in high-computational complexity of the algorithms used for training the models. In this paper, a novel algorithm for point to point prediction of the generated power based on the least squares support vector machine (LS-SVM) has been proposed which can handle the large training database with a very fewer deal of computation and benefits from reasonable accuracy and generalization capability. </p>

scholarly journals Environmental substantiation for the use of alternative energy sources

2021 ◽  
Vol 244 ◽  
pp. 01007
Author(s):  
Svetlana Ovchinnikova ◽  
Aleksandr Borovkov ◽  
Galina Kukinova ◽  
Nina Markina
Keyword(s):  
Renewable Energy ◽  
Intelligent Systems ◽  
Alternative Energy ◽  
Fossil Fuels ◽  
Predictive Analytics ◽  
Renewable Energy Sources ◽  
Climatic Conditions ◽  
Household Waste ◽  
Energy Sources ◽  
Alternative Energy Sources

An overview of the substantiation of the relevance of the transition to mass ecological housing construction, which is determined by an acute shortage of housing and a high increase in the cost of electricity, is given. The development of an ecological substantiation of an energy-independent house using the example of a one-story house, taking into account natural and climatic conditions, is presented. The characteristics of the work of all utilized engineering systems are considered. It has been established that the housing and utilities sector, being one of the main sources of air and groundwater pollution, creates a large amount of household waste, which has a detrimental effect on the environmental situation. Renewable energy sources have an inexhaustible supply, since they are obtained from natural processes that will not be exhausted in the foreseeable future. Thus, the prospects for renewable energy sources are in considering them to replace fossil fuels. Economic efficiency is defined, which implies that the energy source is economical both in relation to the net cost of production and in relation to supply. The development of renewable energy sources will play an important role in the transformation and digitalization of the Russian power industry. Technologies of energy storage, intelligent systems for forecasting production and demand, predictive analytics of equipment condition, consumption management and many others will be developed.

scholarly journals Sustainable Business Strategies as an Element Influencing Diffusion on Innovative Solutions in the Field of Renewable Energy Sources

Energies ◽  
2021 ◽  
Vol 14 (17) ◽  
pp. 5453
Author(s):  
Ewa Stawicka
Keyword(s):  
Renewable Energy ◽  
Social Responsibility ◽  
Diffusion Of Innovation ◽  
Renewable Energy Sources ◽  
Business Strategies ◽  
Energy Sources ◽  
Sustainable Business ◽  
Building Trust ◽  
Innovative Solutions ◽  
The Impact

This article aims to examine the impact of corporate social responsibility, trust, and sustainable business strategies on the diffusion of innovative solutions in renewable energy sources. In this context, the material from the edition of the reports of the Polish Agency for Enterprise Development on innovation in the renewable energy industry was analyzed. A survey was also conducted among enterprises from the SME sector on the creation of a business model taking into account the diffusion of innovation in the field of renewable energy sources. The SME sector consists of entities that usually do not have an extensive organizational structure or research and development teams. Nevertheless, in the current economic situation, it is required that they are highly competitive, including through implemented innovations. Conscious participation of SME entities in the process of diffusion of innovation may be a solution that brings innovative solutions closer. The author stated that social responsibility had a moderate impact on the diffusion of innovation in the field of renewable energy in the SME sector, as it contributed the most to building trust in uncertain energy sources. On the other hand, the study conducted by the author showed that greater experience in the field of social responsibility (the company has a CSR department, there is a person responsible for CSR in the company, the company has a CSR policy, the company has a Code of Ethics, social reports are prepared in the company) had a positive relationship with building trust and commitment to innovative activities related to renewable energy sources. Conscious participation of SME entities in the process of diffusion of innovation may be a solution that approximates innovative solutions.

A SURVEY OF TOURISM MANAGEMENT ATTITUDES TO RENEWABLE ENERGY SUPPLY IN PRIMORSKO-GORANSKA COUNTY (CROATIA)

2007 ◽  
Vol 14 (1) ◽  
pp. 95
Author(s):  
Rade Knezević ◽  
Leo Vičić
Keyword(s):  
Information Technology ◽  
Renewable Energy ◽  
Energy Consumption ◽  
Energy Supply ◽  
Renewable Energy Sources ◽  
Hot Water ◽  
Energy Sources ◽  
Sustainable Business ◽  
Tourism Management ◽  
Consumption Control

The paper presents the methodology of a conducted questionnaire survey and the results regarding energy consumption in the tourism of Primorsko-Goranska County (Croatia). The attitudes about energy consumption management and practical aspects concerning consumption are highlighted. The pool consists of three major groups of tourism objects: hotels and related facilities, camping parks and marinas. The plan was to analyze 91 tourism object, but only from 30 objects was achieved the response (33% rate). Largest share of the pool is located in the coastal area (73%), and much smaller shares are in the mountain region (13%) and the islands (13%). The results of analysis show that the largest amount of energy is used for interior heating/cooling (26,0%) and food purposes (24,5%), then for the illumination (17,3%), hot water (17,0%), laundering and ironing (10,4%), cleaning and waste disposal (2,6%) and other (2,1%). The attitudes about saving are emphasized and 96,7% of surveyed managers suppose that it is possible to manage the energy consumption and that energy increasingly influences their sustainable business activities. Information technology equipment for energy consumption control was installed in 16,7% of facilities and only 13,3% of businesses were exploiting renewable energy sources (RES) in 2007.

scholarly journals A Demand-Response System for Sustainable Manufacturing Using Linked Data and Machine Learning

2021 ◽  
pp. 155-181
Author(s):  
Hendro Wicaksono ◽  
Tina Boroukhian ◽  
Atit Bashyal
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Renewable Energy ◽  
Demand Response ◽  
Short Term Memory ◽  
Renewable Energy Sources ◽  
Heterogeneous Systems ◽  
Energy Sources ◽  
Demand Response Program ◽  
Mapping Techniques

AbstractThe spread of demand-response (DR) programs in Europe is a slow but steady process to optimize the use of renewable energy in different sectors including manufacturing. A demand-response program promotes changes of electricity consumption patterns at the end consumer side to match the availability of renewable energy sources through price changes or incentives. This research develops a system that aims to engage manufacturing power consumers through price- and incentive-based DR programs. The system works on data from heterogeneous systems at both supply and demand sides, which are linked through a semantic middleware, instead of centralized data integration. An ontology is used as the integration information model of the semantic middleware. This chapter explains the concept of constructing the ontology by utilizing relational database to ontology mapping techniques, reusing existing ontologies such as OpenADR, SSN, SAREF, etc., and applying ontology alignment methods. Machine learning approaches are developed to forecast both the power generated from renewable energy sources and the power demanded by manufacturing consumers based on their processes. The forecasts are the groundworks to calculate the dynamic electricity price introduced for the DR program. This chapter presents different neural network architectures and compares the experiment results. We compare the results of Deep Neural Network (DNN), Long Short-Term Memory Network (LSTM), Convolutional Neural Network (CNN), and Hybrid architectures. This chapter focuses on the initial phase of the research where we focus on the ontology development method and machine learning experiments using power generation datasets.

scholarly journals Machine Learning-Based Approach to Predict Energy Consumption of Renewable and Nonrenewable Power Sources

Energies ◽  
2020 ◽  
Vol 13 (18) ◽  
pp. 4870 ◽  
Author(s):  
Prince Waqas Khan ◽  
Yung-Cheol Byun ◽  
Sang-Joon Lee ◽  
Dong-Ho Kang ◽  
Jin-Young Kang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Renewable Energy ◽  
Renewable Energy Sources ◽  
Electricity Consumption ◽  
Energy Sources ◽  
Support Vector ◽  
Power Sources ◽  
Variable Nature ◽  
Soft Computing Techniques ◽  
Nonrenewable Energy

In today’s world, renewable energy sources are increasingly integrated with nonrenewable energy sources into electric grids and pose new challenges because of their intermittent and variable nature. Energy prediction using soft-computing techniques plays a vital role in addressing these challenges. As electricity consumption is closely linked to other energy sources such as natural gas and oil, forecasting electricity consumption is essential for making national energy policies. In this paper, we utilize various data mining techniques, including preprocessing historical load data and the load time series’s characteristics. We analyzed the power consumption trends from renewable energy sources and nonrenewable energy sources and combined them. A novel machine learning-based hybrid approach, combining multilayer perceptron (MLP), support vector regression (SVR), and CatBoost, is proposed in this paper for power forecasting. A thorough comparison is made, taking into account the results obtained using other prediction methods.

Renewable Energy Sources

IEE Review ◽  
1991 ◽  
Vol 37 (4) ◽  
pp. 152
Author(s):  
Kenneth Spring
Keyword(s):  
Renewable Energy ◽  
Renewable Energy Sources ◽  
Energy Sources
Sign in / Sign up

Export Citation Format

Share Document