Future Energy Investment Research Articles

Predictive Modeling of Energy Consumption in the Steel Industry Using CatBoost Regression: A Data-Driven Approach for Sustainable Energy Management

This article presents a machine learning model for predicting energy consumption in the steel industry, which aids in energy management, cost reduction, environmental regulation compliance, informed decision-making for future energy investments, and contributes to sustainability. The dataset used for the prediction model comprises 11 attributes and 35,040 instances. The CatBoost prediction algorithm was employed for energy consumption prediction, and hyperparameter optimization was performed using GridSearchCV with 5-fold cross-validation. The developed model has undergone a comparative analysis based on both Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE) metrics, demonstrating its promise for accurate energy consumption prediction on both the training and test sets. The proposed model accurately predicts energy consumption for different load types, achieving impressive results on both the training set (RMSE=0.382, R2=0.999, MAPE=1.139) and the test set (RMSE=1.073, R2=0.998, MAPE=1.142). These findings highlight the potential of CatBoost as a valuable tool for energy management and conservation, enabling organizations to make informed decisions, optimize resource allocation, and promote sustainability.

Analyzing the Brazilian project of energy expansion

The Brazilian energy grid encompasses mainly hydroelectric power, despite investments in other energy sources. The country’s energy forecast is based on the demand and the contracted projects to supply the consumers’ needs. The present study aimed to analyze the Brazilian investment in energy based on the installed capacity and the country’s forecast. The research question evaluated whether the future energy investments comply with the country’s agreement in reducing emissions and focusing on sustainable development. Primary data were retrieved from governmental open sources and organized. The dependent variables were the data on installed capacity in 2021 (MW), forecast capacity for 2024 (MW), and growth (%). Applying the random forest model, data mining using the Rapidminer Studio was applied to the database. Decision tree algorithms were obtained involving the studied variables. The accuracy was 68% and kappa (<i>κ</i>)=0.60 (prediction result is suitable when accuracy is ≥60%, and <i>κ</i>≥0.60). Three decision tree models were selected to represent the chosen attributes based on the coherence of the decision flow amongst the studied variables. Using data mining, the prediction models of the energy investment in Brazil show the energy forecast for 2024. The current study points out that future investments in energy sources in the electric grid in Brazil aim for diversity since it plans for solar and wind energy sources; nevertheless, it also includes thermal and hydroelectric energy sources.

EMAS III-based analysis of European eco-management for energy efficiency investments

PurposeThe aim is to analyze the European eco-management because the global warming has become a topical issue impacting the whole world. Individual countries are trying to minimize all the catalysts of global warming, such as carbon emissions. This paper addresses this issue and analyzes the performance of European eco-management for the purpose of future energy investments being environmentally.Design/methodology/approachThis paper develops a fuzzy decision-making model to study the performance indicators of selected countries based on EMAS III standard. It employs interval type-2 fuzzy DEMATEL to evaluate the performance factors and TOPSIS methodology to assess five selected European countries' performance in relation to eco-friendly, emission and renewable energy.FindingsEco-friendly energy plays the most critical role in this respect followed by emissions and renewable energy which constitute significant factors. The novelty of this study is identifying significant criteria regarding environmental and energy efficiency of investments and making performance assessments of European countries with a new fuzzy decision-making model. Both expert opinions and datasets are used for the analysis. This paper supports previous research about energy efficiency investments in Europe.Research limitations/implicationsThe innovative feature of this study is identifying significant criteria regarding environmental and energy efficiency of investments and assessing the performance of European countries with a new fuzzy decision-making model. The fact that the analysis only concerns the European region is an important limitation. In future analyses, other groups of countries can be examined. Innovations can be made regarding the method applied. In this context, analyses can be done utilizing different fuzzy numbers. Finally, the importance of the criteria can be calculated with other methods such as SWARA.Practical implicationsThe paper fills the gap in performance analysis of European eco-management for environmentally friendly and efficient energy investments is done in this manuscript.Originality/valueAnalysis of European eco-management performance was done for environmentally friendly and efficient energy investments. A fuzzy decision-making model is constructed. The paper fills the gap in performance analysis of European eco-management for environmentally friendly and efficient energy investments.

An interpretable forecasting framework for energy consumption and CO2 emissions

It is a well-established fact that energy consumption and production, as the primary sources of greenhouse gases, contribute to climate change and global warming issues. The analysis and estimation of the factors that contribute to these harmful gases will be of great assistance in the development of policies to reduce carbon dioxide emissions. In addition to identifying the factors related to energy consumption and CO2 emissions, forecasting the variable of interest as accurately as possible has a key role in increasing the efficiency of energy strategies to be implemented. Unlike studies in the literature, this study not only forecasts the future value of energy consumption and CO2 emissions but also determines the relationship between the predictions and the influential variables by revealing the contribution of each variable to the prediction. For this purpose, the study proposes an interpretable forecasting framework based on values of the Shapley additive explanation (SHAP) to provide a simpler explanation of machine learning (ML) models in forecasting energy consumption and CO2 emissions. The results obtained show that the total electricity generation from different energy sources is found to be the most important variable interacting positively with both energy consumption and CO2 emissions. Also, the influence of the predictors on projections made before and after COVID-19 has changed dramatically. The proposed method may assist policymakers in making future energy investments and establishing energy laws more accurately and efficiently as it explains the drivers of the forecasts.

Exploring Patterns of Transportation-Related CO2 Emissions Using Machine Learning Methods

While the transportation sector is one of largest economic growth drivers for many countries, the adverse impacts of transportation on air quality are also well-noted, especially in developing countries. Carbon dioxide (CO2) emissions are one of the direct results of a transportation sector powered by burning fossil-based fuels. Detailed knowledge of CO2 emissions produced by the transportation sectors in various countries is essential for these countries to revise their future energy investments and policies. In this framework, three machine learning algorithms, ordinary least squares regression (OLS), support vector machine (SVM), and gradient boosting regression (GBR), are used to forecast transportation-based CO2 emissions. Both socioeconomic factors and transportation factors are also included as features in the study. We study the top 30 CO2 emissions-producing countries, including the Tier 1 group (the top five countries, accounting for 61% of global CO2 emissions production) and the Tier 2 group (the next 25 countries, accounting for 35% of total CO2 emissions production). We evaluate our model using four-fold cross-validation and report four frequently used statistical metrics (R2, MAE, rRMSE, and MAPE). Of the three machine learning algorithms, the GBR model with features combining socioeconomic and transportation factors (GBR_ALL) has the best performance, with an R2 value of 0.9943, rRMSE of 0.1165, and MAPE of 0.1408. We also find that both transportation features and socioeconomic features are important for transportation-based CO2 emission prediction. Transportation features are more important in modeling for 30 countries, while socioeconomic features (especially GDP and population) are more important when modeling for Tier 1 and Tier 2 countries.

Forecasting of transportation-related energy demand and CO2 emissions in Turkey with different machine learning algorithms

Adverse impacts of the transportation sector on not only air quality but also economic growth of a country are nowadays well-noticed, particularly by developing countries. Today, the transportation sector is powered by burning the fossil-based fuels at more than 99% and approximately 6.5 million deaths annually occur due to air-pollution-related diseases worldwide. Therefore, knowledge of both energy demand and CO 2 emission of a country is a very significant issue in order to revise its future energy investments and policies. In this framework, three machine learning algorithms (deep learning (DL), support vector machine (SVM), and artificial neural network (ANN)) are used to forecast the transportation-based-CO 2 emission and energy demand in Turkey. The gross domestic product per capita (GDP), population, vehicle kilometer, and year are used as input parameters in the study. It is noticed that there is a very high correlation among year, economic indicators, population, vehicle kilometer, transportation-based energy demand, and CO 2 emissions. To present a better comparison, the results of these algorithms are discussed with six frequently used statistical metrics (R 2 , RMSE, MAPE, MBE, rRMSE, and MABE). For all machine learning algorithms, R 2 values are varying between 0.8639 and 0.9235, and RMSE is smaller than 5 × 10 6 tons for CO 2 emission and 2 Mtoe for energy demand. According to the classifications in the literature, the forecast results are generally categorized as "excellent" for rRMSE metric (<10%), and “high prediction accuracy” for MAPE metric (<10%). On the other hand, with two mathematical models, future energy demand and CO 2 emission arising from the transportation sector in Turkey are forecasted by the year 2050. In the results, it is forecasted that the annual growth rate for transportation-related energy demand and CO 2 emission in Turkey cumulatively rise by 3.7% and 3.65%, respectively. Both energy demand and CO 2 emissions from the transportation sector in Turkey will increase nearly 3.4 times higher in the year 2050 than those of today. In conclusion, the paper clearly reports that the future energy investments of the country should be revised, and various policies, regulations, norms, restrictions, legislations, and challenges on both energy consumption and emission mitigation from the transportation sector should be established by the policy-makers.

A novel stochastic model for very short-term wind speed forecasting in the determination of wind energy potential of a region: A case study from Turkey

The dominant use of fossil fuels in energy production and the great damage they have caused to both environment and human health are nowadays well-noticed. This dominant use of fossil fuels brings with it problems such as energy shortage, environmental pollution, and ecological degradation. Recently, there has been a tremendous turn towards renewable energy sources in the future energy investments of the governments. Of renewable energy sources, wind energy has located at a significant owing to its abundance, infinity, independence, reliability, and clearance characteristics. However, it is of great importance of forecasting both the amount and prices of electricity for suppliers, particularly in intraday markets, because it is a serious problem for suppliers to generate more or less electricity than they guarantee. They may face sanctions or lower profit margin risks owing to unbalances between the forecast and generate values. Accordingly, a novel model - variance sensitive exponential smoothing model - (VSES) was proposed in the present study and then compared with three well-known models in the literature namely Trigg & Leach (T-L), Pantazopoulos and Pappis (P-P) and optimized simple exponential smoothing (o-SES). Then the wind speed dataset of Çeşme district (in İzmir, Turkey) in 30-minute, 1-hour, and 3-hour time horizons are tested with these four models. In the comparison of the models, four statistical benchmarks (MAD, MSE, MAPE, and RMSE) are discussed. In the results, the RMSE value of all models is smaller than 0.9 m/s for all forecasting horizons. VSES model proposed in this study has exhibited the very satisfying MAD, MSE, MAPE, and RMSE values of 0.318 m/s, 0.183 m/s, 14.60%, and 0.427 m/s, respectively for 30-minute and 0.382 m/s, 0.258 m/s, 16.29%, and 0.506 m/s, respectively for 1-hour, respectively. On the other hand, the best model for 3-hour time horizon is achieved for the P-P model with the best MAD, MSE, MAPE, and RMSE values of 0.574 m/s, 0.543 m/s, 26.38%, and 0.729 m/s, respectively, and it is closely followed by the VSES model with 0.601 m/s, 0.614 m/s, 26.72%, and 0.775 m/s, respectively. In conclusion, it is clearly observed that the VSES model proposed in this study can be successfully applied for the determination of the wind energy potential of the regions, and effectively used in intraday markets particularly for very short-term wind speed forecasting.

Assessment of machine learning, time series, response surface methodology and empirical models in prediction of global solar radiation

Solar radiation (SR) knowledge plays a vital role in the design, modelling, and operation of solar energy conversion systems and future energy investment policies of the governments. However, these data are not measured for all regions due to the non-availability of SR measurement equipment at the weather stations. Therefore, SR has to be accurately predicted using various prediction models. In this research, four models from different classes are being used to predict monthly average daily global SR data. The models used in this study are based on a machine-learning algorithm (feed-forward neural network), empirical models (3 Angstrom-type models), time series (Holt-Winters), and mathematical model (RSM). As the prediction locations, four provinces (Ankara, Karaman, Kilis, and Şırnak) in Turkey are selected. The dataset including pressure, relative humidity, wind speed, ambient temperature, and sunshine duration is supplied from the Turkish State Meteorological Service and it covers the years 2008–2018. In the study, monthly average daily global SR data for the year 2018 is being predicted, and the performance success of the models is discussed in terms of the following benchmarks R2, MBE, RMSE, MAPE, and t-stat. In the results, R2 value for all models is varying between 0.952 and 0.993 and MAPE and RMSE value for all models is smaller than 10% and 2 MJ/m2-day, respectively. Evaluation in terms of t-stat value, no models exceed the t-critic limit. Considering all the models together, ANN has presented the best results with an average R2, MBE, RMSE, MAPE, and t-stat of 0.9911, 0.1323 MJ/m2-day, 0.78 MJ/m2-day, 4.9263%, and 0.582, respectively. Then Holt-Winters, RSM, and empirical models closely followed it, respectively.

Forecasting of daily natural gas consumption on regional basis in Turkey using various computational methods

It is widely accepted that natural gas is a clean energy source that can be used to meet energy demand for heating and industrial purpose among the fossil fuels and its usage remarkably increases in order to maintain a clean environment in many countries in the world. It is fact that this makes energy investment planning in a country or region highly important for suitable economic development as well as environmental aspect. Therefore, energy demand for various sectors should be estimated in the frame of short-term energy policy. For accurate estimation of short-term energy demand a limited number of computational methods are employed by using the 4 yearly measured natural gas consumption values. Among these methods, the ANN and time series are widely used for short-term estimation of natural gas consumption in Turkey's certain regions. In this study, multilayer perceptron the ANNs with time series approach is proposed to forecast short-term natural gas consumption. Meteorological data (moisture, atmospheric pressure, wind speed and ambient temperature) obtained from the regional gas distribution company and the local meteorology office in last 4 years to construct well-tuned algorithm. Although the number of data was small, the proposed algorithm works well to forecast the short-term natural gas consumption and produces encouraging and meaningful outcomes for future energy investment policy.

Energy efficiency as an inexhaustible energy resource with perspectives from the U.S. and Turkey

We are familiar with fossil fuels as the primary energy resources, but the time has come to firmly establish energy efficiency as an important energy resource to be considered in future energy investment decisions. Energy efficiency is already being touted as the ‘6. Fuel’ after coal, oil, natural gas, nuclear energy, and renewable energy (hydroelectric, wind, solar, geothermal, etc). Energy efficiency is also the cheapest resource: The cost of electricity obtained from energy efficiency is usually between 1 and 3 cents (U.S.) per kWh. Further, energy efficiency is local and labor intensive with significant benefits to the environment by displacing pollution. The impact and importance of energy efficiency is well-established in developed countries, but this is not yet the case in developing nations. Therefore, there is a need to raise awareness about energy efficiency, and this is best done by publicizing the successful applications and their impacts. As a striking example, if the refrigerators in the U.S. were to consume electric power at the 1974 levels, the U.S. would need about 30 000 MW of additional installed power to meet this extra demand, which is equivalent to the peak power of Turkey—a country of 70 million. This means 60 coal plants with an average rated power of 500 MW and a construction cost of about $60 billion. Also, the conservation measures that were put in place in 1970s and 1980s in the U.S. became sufficient to meet most power needs of the growing economy, and consequently, a total of 97 nuclear power plants at different stages of construction with a total capacity of 107 000 MW were cancelled. Energy efficiency continues to be recognized as a major energy resource. As detailed in its energy policy report Vision 2025, the U.S. plans to meet at least 50% of the expected future load growth by energy efficiency. The developing nations should take notice of these developments and give energy efficiency the highest priority in energy investment decisions to meet growing demand. Copyright © 2010 John Wiley & Sons, Ltd.

Energy investment: quantity or quality

The World Energy Investment Outlook, published by the IEA in November 2003, questions the conditions for financing future energy investments, especially in developing countries for the power sector, and for energy exports to the OECD. Mitchell reviews the role of governments in energy investment in OECD countries. This leads to a conventional wisdom about conditions which OECD investors would like to see, but not much guidance on issues of resource rents or conflicts arising from differences between local costs and national benefits. An "Institutional agenda" of initiatives (WTO, TRIMS, GATS and the Energy Charter Treaty) seeks to export OECD conditions and promote "good governance" in developing countries. There is also a "Behavioral agenda" in which international companies are persuaded to promote a variety of millennium goals, including human rights, poverty reduction, and environmental sustainability. These factors have different relevance in different countries in which major energy investments are foreseen. Finally, Mitchell suggests that "acceptability" is an inclusive concept under which the OECD type initiatives can be combined with regard for the circumstances and interests of the different non-OECD countries, to give an operational definition of investment "quality."