Energy Production Data Research Articles

Recurrence Multilinear Regression Technique for Improving Accuracy of Energy Prediction in Power Systems

This paper demonstrates how artificial intelligence can be implemented in order to predict the energy needs of daily households using both multilinear regression (MLR) and single linear regression (SLR) methods. As a basic implementation, the SLR makes use of one input variable, which is the total amount of energy generated as an input. The MLR implementation involves multiple input variables being taken from various energy sources, including gas, coal, geothermal, wind, water, biomass, oil, etc. All of these variables are derived from detailed energy production data from the various energy sources. The purpose of this paper is to demonstrate that it is possible to analyze energy demand and supply directly together as a way to produce a more in-depth analysis. By analyzing energy production data from previous periods of time, a prediction of energy demand can be made. Compared to the SLR implementation, the MLR implementation is found to perform better because it is able to achieve a smaller error value. Furthermore, the forecasting pattern is carried out sequentially based on a periodic pattern, so this paper calls this method the recurrence multilinear regression (RMLR) method. This paper also creates a pre-clustering using the K-Means algorithm before the energy prediction to improve accuracy. Other models such as exponential GPR, sequential XGBoost, and seq2seq LSTM are used for comparison. The prediction results are evaluated by calculating the MAE, RMSE, MAPE, MAPA, and time execution for all models. The simulation results show that the fastest and best model that obtains the smallest error (3.4%) is the RMLR clustered using a weekly pattern period.

SUSTAINABLE GROWTH THROUGH GREEN ECONOMY: AN ECONOMETRIC ANALYSIS

Objective: In recent years, the growing interest and awareness of environmental issues have once again brought the issue of population growth to the forefront, exerting significant pressure on production. The increased pressure on production and the resultant rise in production have led to a faster trend in consumption. As a natural consequence of both trends, detrimental effects on ecological balance and the environment have begun to emerge. The concept of "green economy" has emerged as an approach to restore the disrupted ecological balance and operationalize this order. This widely spreading concept has begun to generate significant policies at both national and international levels. Method: This study aims to examine the relationship between renewable energy production, consumption, wastewater, and solid waste data, covering the years 1980-2022, using econometric methods to explore the relationship between green economy and economic growth in the context of Turkey. The objective is to analyze the impact of the recent and rapidly increasing transformation towards "green economy" on the long-term trajectory of economic growth in Turkey. Result and Discussion: Based on the findings of the study, it has been determined that, at the 0.05 level of statistical significance, there is no long-term relationship between the variables and economic growth, as well as no long-term significant relationship between any of the variables. When the statistical significance is accepted at the 0.10 level, a significant long-term relationship is identified between the amount of wastewater and renewable energy, with a 1% increase in wastewater leading to a 0.82% increase in renewable energy. Implications: The findings of this study and the transformation towards a green economy, widely covered in the literature, have been recognized in Turkey and an unnamed green transformation movement has begun. As the results of the analysis of the study indicate, the policies introduced for the transition to a green economy and transformation efforts have a positive and significant impact on economic growth in the long term, but the same cannot be claimed for the short term, but in the future, advances in scientific and technological progress and over-specialization will inevitably have positive consequences on global and domestic sustainable development in the short term. Originality/Value: The distinctive feature of this study from similar studies in the literature is that the relationship between the green economy and sustainable growth was analyzed empirically and with five separate econometric models. Recommendations: Turkey needs serious reforms in education in its transition to a green economy. In addition, there is a need for political determination to use environmentally friendly sectors that are more open to innovation and competition, such as renewable energy and green building sectors.

Machine learning-based energy management and power forecasting in grid-connected microgrids with multiple distributed energy sources

The growing integration of renewable energy sources into grid-connected microgrids has created new challenges in power generation forecasting and energy management. This paper explores the use of advanced machine learning algorithms, specifically Support Vector Regression (SVR), to enhance the efficiency and reliability of these systems. The proposed SVR algorithm leverages comprehensive historical energy production data, detailed weather patterns, and dynamic grid conditions to accurately forecast power generation. Our model demonstrated significantly lower error metrics compared to traditional linear regression models, achieving a Mean Squared Error of 2.002 for solar PV and 3.059 for wind power forecasting. The Mean Absolute Error was reduced to 0.547 for solar PV and 0.825 for wind scenarios, and the Root Mean Squared Error (RMSE) was 1.415 for solar PV and 1.749 for wind power, showcasing the model’s superior accuracy. Enhanced predictive accuracy directly contributes to optimized resource allocation, enabling more precise control of energy generation schedules and reducing the reliance on external power sources. The application of our SVR model resulted in an 8.4% reduction in overall operating costs, highlighting its effectiveness in improving energy management efficiency. Furthermore, the system’s ability to predict fluctuations in energy output allowed for adaptive real-time energy management, reducing grid stress and enhancing system stability. This approach led to a 10% improvement in the balance between supply and demand, a 15% reduction in peak load demand, and a 12% increase in the utilization of renewable energy sources. Our approach enhances grid stability by better balancing supply and demand, mitigating the variability and intermittency of renewable energy sources. These advancements promote a more sustainable integration of renewable energy into the microgrid, contributing to a cleaner, more resilient, and efficient energy infrastructure. The findings of this research provide valuable insights into the development of intelligent energy systems capable of adapting to changing conditions, paving the way for future innovations in energy management. Additionally, this work underscores the potential of machine learning to revolutionize energy management practices by providing more accurate, reliable, and cost-effective solutions for integrating renewable energy into existing grid infrastructures.

Predicting Energy Production in Renewable Energy Power Plants Using Deep Learning

It is very important to analyze and forecast energy production for investments in renewable energy resources. In this study, the energy production of wind and solar power plants, which are among the leading renewable energy sources, was estimated using deep learning. For a solar power plant, three different solar power plants with 1MW installed power were examined. Three-year energy production data of power plants were taken. These data were used with the deep learning method long short-term memory (LSTM) and seasonal autoregressive moving average (SARIMA). Results were obtained for each dataset; they were subjected to five different (MSE, RMSE, NMSE, MAE, and MAPE) error performance measurement systems. In the LSTM model, the highest accuracy rate was 81% and the lowest accuracy rate was 59%. In the SARIMA model, the highest accuracy rate was 66% and the lowest accuracy rate was 41%. As for wind energy, wind speeds in two different places were estimated. Wind speed data were taken from meteorological stations. Datasets were tested with MAPE, R2, and RMSE error performance measurement systems. LSTM, GRU, CNN-LSTM, CNN-RNN, LSTM-GRU, and CNN-GRU deep learning methods were used in this study. The CNN-GRU model achieved a maximum accuracy of 99.81% in wind energy forecasting.

Concurrent PV production and consumption load forecasting using CT‐Transformer deep learning to estimate energy system flexibility

AbstractThe integration of renewable energy sources (RESs) into power systems has increased significantly due to technical, economic, and environmental factors, necessitating greater flexibility to manage variable consumption loads and renewable energy generation. Accurate forecasting of solar energy production and consumption load is critical for enhancing power system flexibility. This study introduces a novel deep learning model, a spatial‐temporal hybrid convolutional‐transformer (CT‐Transformer) network with unique features and extended memory capacity. Additionally, a flexibility index (FI) is introduced to evaluate power system flexibility (PSF) based on the forecasting results. The CT‐Transformer forecasts PSF for the next 24 and 168 hours, using the FI to evaluate PSF based on forecasting results. The input data includes meteorological, solar energy production, load demand, and pricing data from France, comprising hourly data from 2015 and 2016 (about 17,520 entries). Data preprocessing involves correcting incomplete and irrelevant segments. The CT‐Transformer's performance is compared to other deep learning techniques, showing superior results with the lowest prediction error (2.5%) and a maximum error of 10.1% (MAE). It also achieved a prediction error of 0.08% for system flexibility, compared to the highest error of 0.96%. This research highlights the CT‐Transformer's potential for accurate RES and load forecasting and PSF evaluation.

Uncertain statistics models for characterizing annual total energy production

In the field of annual total energy production, it is hard to determine whether the experimental data is linear or nonlinear growth characteristics. Therefore, different regression models are used to test the properties of the observed data. This article uses actual data to perform parameter estimation and residual analysis of linear and nonlinear regression models. In addition, uncertain nonlinear time series model is used to fit the experimental data in order to find a more suitable statistics model after obtaining the growth properties of the observed data. A test is introduced to prove that the disturbance terms presented in the regression and time series models are actually uncertain variables, not random variables. Moreover, the uncertain hypothesis tests show that the uncertain time series model is in better agreement with the real data of the annual total energy production.

Feature Selection by Binary Differential Evolution for Predicting the Energy Production of a Wind Plant

We propose a method for selecting the optimal set of weather features for wind energy prediction. This problem is tackled by developing a wrapper approach that employs binary differential evolution to search for the best feature subset, and an ensemble of artificial neural networks to predict the energy production from a wind plant. The main novelties of the approach are the use of features provided by different weather forecast providers and the use of an ensemble composed of a reduced number of models for the wrapper search. Its effectiveness is verified using weather and energy production data collected from a 34 MW real wind plant. The model is built using the selected optimal subset of weather features and allows for (i) a 1% reduction in the mean absolute error compared with a model that considers all available features and a 4.4% reduction compared with the model currently employed by the plant owners, and (ii) a reduction in the number of selected features by 85% and 50%, respectively. Reducing the number of features boosts the prediction accuracy. The implication of this finding is significant as it allows plant owners to create profitable offers in the energy market and efficiently manage their power unit commitment, maintenance scheduling, and energy storage optimization.

Comparison of Actual Results and PVSyst Simulation in the Design of Off-Grid Solar Power Generation System (PLTS) in Karuni Village, Southwest Sumba

This research aims to compare the actual production with the simulations using the PVSyst software for the Off-Grid Solar Power Plant (PLTS) in Karuni Village, Southwest Sumba. The Off-Grid PLTS in Karuni Village is a vital solution for improving remote areas' electricity access. Actual energy production data from the PLTS were obtained from monitoring systems, while simulation results were obtained through PVSyst. The analysis results indicate a difference of approximately 10% between the actual and simulated results. It observed that it is influenced by variability in local weather conditions, maintenance, system management levels, and limitations of the simulation model. The implications of this research emphasize the importance of using accurate data in simulations, improving PLTS system maintenance, and developing more sophisticated simulation models. Recommendations for further research include further analysis of factors influencing the differences in results. This study provides valuable insights into the planning and management of Off-Grid PLTS. It offers perspectives on enhancing the accuracy of future PLTS system planning and management.

Improving Sustainable Energy Distribution in the USA: Leveraging the Theory of Constraints’ Thinking Process Tools

This study explores the improvement of sustainable energy distribution in the USA through the application of the Theory of Constraints’ Thinking Process Tools. The research adopts a mixed-methods approach, combining quantitative analysis of energy production, consumption, and transmission data with qualitative analysis of stakeholder perspectives and regulatory frameworks. The findings reveal key challenges, including infrastructure bottlenecks, regulatory barriers, and coordination issues among stakeholders. Through the application of the Theory of Constraints’ tools, targeted strategies are developed to address these constraints and enhance the efficiency, reliability, and sustainability of the energy distribution system. Recommendations are provided to invest in infrastructure upgrades, streamline regulatory processes, foster stakeholder collaboration, prioritize research and innovation, and promote public awareness and engagement. By implementing these recommendations, stakeholders can work together to overcome existing challenges and accelerate the transition towards a more sustainable energy future in the USA.

A COMPREHENSIVE TIME SERIES ANALYSIS OF ANNUAL FOSSIL-BASED ELECTRICAL ENERGY PRODUCTION ACROSS KEY NATIONS (1985-2022)

This study addresses the critical challenge of accurately predicting annual electrical energy production derived from fossil sources across diverse nations, including Türkiye, Germany, England, France, Iran, and Ukraine spanning the years 1985 to 2022. Employing a combination of advanced deep learning techniques and traditional statistical models, the research aims to enhance forecasting precision and contribute to a deeper understanding of global energy consumption dynamics. By leveraging time series analysis, the study captures the intricate temporal patterns inherent in fossilbased energy production data, enabling robust predictions over an extended historical timeframe. Performance evaluation metrics such as Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) are utilized to rigorously assess the accuracy and reliability of the developed forecasting models. The scientific significance of this study lies in its ability to provide valuable insights into the complex interplay between socio-economic factors, technological advancements, and environmental considerations shaping fossil-based energy production trends. By conducting a comparative analysis across multiple countries with distinct energy landscapes, the research not only elucidates regional variations but also underscores the need for tailored forecasting approaches to address unique contextual challenges. The findings of this study hold significant implications for energy policymakers, industry stakeholders, and researchers striving to optimize resource allocation, mitigate environmental impacts, and transition towards sustainable energy systems. By advancing the state-of-the-art in energy forecasting methodologies, this research contributes to the broader goal of achieving a more resilient, equitable, and environmentally sustainable energy future.

SDG7 and renewable energy consumption: The influence of energy sources

The transition to renewable energy poses a significant contemporary challenge. To address this, understanding the determinants of this transition is pivotal. This study examines renewable energy consumption in the Eurozone from 2000 to 2019 using panel data analysis and the Driscoll-Kraay technique with time-fixed effects. Two models test: one with aggregated energy production data and another with disaggregated data. Confirming existing literature, a strong inverse association emerges between GDP and renewable energy consumption. A negative relationship arises between Foreign Direct Investment (FDI), energy imports, and renewable energy consumption. Moreover, a direct, robust correlation exists between R&D expenditure and renewable energy consumption. When comparing the two models, the consumption of renewable energy shows a significant association with both the overall volume of electricity production and volumes differentiated by energy sources. Notably, the relationship proves more pronounced in the disaggregated data model. Disaggregated data by energy type provides higher explanatory power, underscoring its value for future studies. Consequently, this study underscores the heightened accuracy and reliability of using disaggregated energy production data in understanding the renewable energy transition.

Distributed Generation Control Using Ripple Signaling and a Multiprotocol Communication Embedded Device

Remotely performing real-time distributed generation control and a demand response is a basic aspect of the grid ancillary services provided by grid operators, both the transmission grid operators (TSOs) and distribution grid operators (DNOs), in order to ensure that voltage, frequency and power loads of the grid remain within safe limits. The stochastic production of electrical power to the grid from the distributed generators (DGs) from renewable energy sources (RES) in conjunction with the newly appeared stochastic demand consumers (i.e., electric vehicles) hardens the efforts of the DNOs to keep the grid’s operation within safe limits and prevent cascading blackouts while staying in compliance with the SAIDI and SAIFI indices during repair and maintenance operations. Also taking into consideration the aging of the existing grid infrastructure, and making it more prone to failure year by year, it is yet of great significance for the DNOs to have access to real-time feedback from the grid’s infrastructure—which is fast, has low-cost upgrade interventions, is easily deployed on the field and has a fast response potential—in order to be able to perform real-time grid management (RTGM). In this article, we present the development and deployment of a control system for DG units, with the potential to be installed easily to TSO’s and DNO’s substations, RES plants and consumers (i.e., charging stations of electric vehicles). This system supports a hybrid control mechanism, either via ripple signaling or through a network, with the latter providing real-time communication capabilities. The system can be easily installed on the electric components of the grid and can act as a gateway between the different vendors communication protocols of the installed electrical equipment. More specifically, a commercially available, low-cost board (Raspberry Pi) and a ripple control receiver are installed at the substation of a PV plant. The board communicates in real-time with a remote server (decision center) via a 5G modem and with the PV plants inverters via the Modbus protocol, which acquires energy production data and controls the output power of each inverter, while one of its digital inputs can be triggered by the ripple control receiver. The ripple control receiver receives on-demand signals with the HEDNO, triggering the digital input on the board. When the input is triggered, the board performs a predefined control command (i.e., lower the inverter’s power output to 50%). The board can also receive control commands directly from the remote server. The remote server receives real-time feedback of the acquired inverter data, the control signals from the ripple control receiver and the state and outcome of each performed control command.

Spatial and Temporal Patterns of Green Energy Development in China

The development of non-conventional energy sources is not only an important guarantee for national energy security but also a key support for the realization of carbon peaking and carbon neutrality goals. However, there is limited knowledge of the spatial and temporal patterns and changing characteristics of green energy development in China. Here, based on the energy production and consumption data of the last decade, we combined the gravity center model and statistical model to assess the spatial and temporal patterns of non-conventional energy in 31 provinces of China. The research results show: (1) under the impetus of the development of green low-carbon and ecological civilization strategy, the rate of increase in the production of non-conventional energy in China and the proportion of it increase year by year, and the energy structure obviously presents the characteristics of being low-carbon and cleaner. (2) For the spatial patterns of non-conventional energy development, due to the development of wind power and photovoltaic constraints by natural resource conditions and technology, their development trend is best in northeast, north, and northwest China. (3) The generation of PV and wind power is dominated by northwest China and north China, and the generation of nuclear power is dominated by southeast China, whereas the consumption is dominated by east China and central China; there is an obvious spatial imbalance between non-conventional energy production and consumption. (4) The proportion of photovoltaics in non-conventional energy production has increased significantly over the 2010–2020 period, which is the main driver of the overall shift in the gravity center of non-conventional energy production towards the northwest region of China. (5) In recent years, the gradual transfer of industries from the east to the central and western regions of China and the improvement in the economic level of the western regions have led to an increase in energy production and consumption, promoting a shift in the gravity center of energy production and consumption to the west. Overall, the structural transformation of China’s energy production and consumption (from a high-carbon black structure to a low-carbon and carbon-free green structure) is progressing, and some provinces have achieved significant results, presenting a non-conventional energy industry in accordance with local conditions and the development trend of the non-conventional energy industry.

Forecasting solar energy production in Spain: A comparison of univariate and multivariate models at the national level

Renewable energies, such as solar power, offer a clean and cost-effective energy source. However, their integration into national electricity grids poses challenges due to their dependence on climate and geography. While numerous studies have focused on solar energy time series, few have specifically addressed the critical task of forecasting solar energy production at the national level. Accurate national-level forecasting is crucial for optimizing energy management, informing policy development, and promoting environmental sustainability. This study aims to address the challenges associated with the significant variability in renewable energy production and its impact on grid stability by improving the accuracy of existing forecasting approaches. To achieve this goal, we evaluate the effectiveness of univariate and multivariate approaches for time series forecasting of national solar energy production data from ESIOS (the Spanish System Operator). Our primary focus is on leveraging external solar variables, such as solar irradiance data. To this end, we propose a methodology to integrate solar irradiance forecasts with historical data from solar power plants in Spain to improve the performance of multivariate models. Subsequently, we compare the performance of classical regression techniques and state-of-the-art deep learning algorithms, presenting univariate and multivariate models for three forecast horizons (1 h, 24 h, and 48 h). Finally, we assess the performance of our best univariate and multivariate models by comparing them with the official forecast of ESIOS. Our findings indicate that the best-performing models are deep-learning multivariate approaches, which benefit from incorporating solar irradiance forecasts, particularly for longer forecast horizons (24 h and 48 h), and avoid the detrimental effects of the Hughes Phenomenon, which seems to hamper non-deep-learning forecasters. The top-performing models, based on Convolutional Networks and Convolutional + Recurrent Neural Networks, outperform ESIOS by reducing mean absolute error by 41% and 47.58%, respectively.

Role of Machine Learning Algorithms for Wind Power Generation Prediction in Renewable Energy Management

The electrical energy demand is growing every day. Fossil fuel-based electrical power generation pollutes the environment. So, to fulfil the electrical energy demand, clean renewable energy sources like solar and wind are emerging as viable alternatives. But the amount of power generated from solar and wind energy sources is varying in magnitude and is intermittent in nature. So, predicting solar and wind power generation is indispensable for efficient planning and controlling of a renewable energy system. This paper aims to identify a machine learning model suitable for time-series prediction of wind power and also surveys the predominantly existing machine learning followed by the deep learning approaches. The main objective of this study is to solve a real-life problem in the renewable energy sector by accurately estimating the amount of power generation production per hour by applying machine learning techniques using historical wind power energy production data. Hence, historical wind data set with input parameters like wind speed, wind direction air temperature, pressure, and output parameters of wind power generation is considered for training and testing using mean absolute error for evaluating the prediction accuracy. Layers from simple to complex deep layers are trained and tested for prediction accuracy, i.e. baseline, linear, dense, multidense, convolution neural networks, and long short-term memory. Investigating further, LSTM and Residual LSTM prove to have higher mean absolute prediction accuracy of 0.0987 and 0.0958. This eventually enlightens the wind power forecasting models and new paths for further research applications.

Performance Ratio and Loss Analysis for a Grid-Connected Solar Photovoltaic System: Case of the 7MW Plant in Malbaza, Niger

The use of solar energy in sunny countries is an efficient way to overcome the energy shortage. The interest of this energy is not only economic but also environmental, as it emits few greenhouse gases. Niger, a vast landlocked country in the Sahel, is characterized by an average sunshine duration of 8.5 hours per day and an estimated average level of sunshine of around 5 to 7 kW/m2 per day. However, the rate of access to electricity in Niger remains very low. To address this problem, a 7MW solar photovoltaic power plant has been built by the State of Niger in the town of Malbaza. It is composed of monocrystalline photovoltaic panels and injects its energy into the national grid. The objective of this study is to compare the performance ratio determined by measurements with that obtained by simulation under PVsyst in order to evaluate its efficiency. The study is based on annual energy production data, recorded at the site, from 1 January 2021 to 31 December 2021. The annual average values of the global irradiation of the site, the energy produced by the system and the energy injected into the grid are respectively 249.86kWh/m², 1225.25MWh and 972.62MWh; those of the ambient temperature and the performance ratio (PR) are respectively 29.78°C and 0.795. The global losses caused by meteorological phenomena and those linked to the technologies of the various components of the system are evaluated at 20.10%.

Multi Dimension-Based Optimal Allocation of Uncertain Renewable Distributed Generation Outputs with Seasonal Source-Load Power Uncertainties in Electrical Distribution Network Using Marine Predator Algorithm

In the last few years, the integration of renewable distributed generation (RDG) in the electrical distribution network (EDN) has become a favorable solution that guarantees and keeps a satisfying balance between electrical production and consumption of energy. In this work, various metaheuristic algorithms were implemented to perform the validation of their efficiency in delivering the optimal allocation of both RDGs based on multiple photovoltaic distributed generation (PVDG) and wind turbine distributed generation (WTDG) to the EDN while considering the uncertainties of their electrical energy output as well as the load demand’s variation during all the year’s seasons. The convergence characteristics and the results reveal that the marine predator algorithm was effectively the quickest and best technique to attain the best solutions after a small number of iterations compared to the rest of the utilized algorithms, including particle swarm optimization, the whale optimization algorithm, moth flame optimizer algorithms, and the slime mold algorithm. Meanwhile, as an example, the marine predator algorithm minimized the seasonal active losses down to 56.56% and 56.09% for both applied networks of IEEE 33 and 69-bus, respectively. To reach those results, a multi-objective function (MOF) was developed to simultaneously minimize the technical indices of the total active power loss index (APLI) and reactive power loss index (RPLI), voltage deviation index (VDI), operating time index (OTI), and coordination time interval index (CTII) of overcurrent relay in the test system EDNs, in order to approach the practical case, in which there are too many parameters to be optimized, considering different constraints, during the uncertain time and variable data of load and energy production.

Experimental Evaluation of the Performance Degradation of a Solar PV Plant Operating in a Sahelian Climate

Niger, a Sahelian country, is known for its extreme climatic conditions and its wealth of solar deposits. In Niger, the average duration of sunshine is 8.5 hours per day and its average level is estimated at about 5 to 7 kW/m² per day. However, the rate of access to electricity in Niger remains very low. To address this problem, a 7MW solar photovoltaic power plant has been built by the authorities in the town of Malbaza (13°58.54 N and 13°58.54 E). It has a capacity of 7 MW and is composed of monocrystalline photovoltaic solar panels. In this study, we propose a method for analysing the degradation of a solar photovoltaic power plant under its operating conditions. The study will help to assess the performance and efficiency of PV systems installed in this geographical area. For this study, we use annual energy production data, recorded at the site, from 2019 to 2021. The method is based on the performance ratio values measured during these three years of operation. An hourly IR energy production index was introduced to study the degradation and reliability of the system. The performance ratio values obtained from the measurements in 2019, 2020 and 2021 are 73.22, 72.73 and 70.84 respectively; the calculated hourly energy production index values are 0.921, 0.914 and 0.891 respectively. The PVsyst software was used to estimate the value of the performance ratio. The value obtained is 79.50%. Thus, a degradation of 1% per year over the three years of operation is estimated. Finally, a comparison was made with other studies.

Implantation, Operation Data and Performance Assessment of An Urban Area Grid-Connected Small Wind Turbine

Over the last few decades, and more prominently currently, many countries have launched and reinforced campaigns to reduce CO2 emissions from all human activities and, in the area of energy, promote energy generating technologies from low carbon, renewable sources, especially wind and solar. In recent years, this promotion of renewables can be seen in statistics as well as an extraordinary increase in plants using renewable sources. There is more activity surrounding the use of small devices installed close to consumers, such as small wind turbines (SWT). In cities, the best places to install SWT are tall buildings. The Institute of Energy and Environment (IEE-USP) has installed a 1.8 kW SWT on the University of São Paulo campus in São Paulo, Brazil. Even with low-magnitude winds at the site, the SWT installation was carried out to serve as a didactic apparatus and demonstration initiative of wind energy generation connected directly to the University’s electric grid, which already has other embedded renewable sources installed, namely photovoltaic and biogas plants. The turbine was placed on the roof of the existing High Voltage Laboratory building, leading to an operating height of 35 m. This paper presents previous local wind data measurements using a Lidar system, annual energy yield estimation calculations, and measurements, also bringing all implementation details. It reports and analyzes the operation and energy production data from three full operational years, from 2018 to 2020, discussing and concluding with further improvements of SWT from technical and economic aspects.

The french (non-)compliance with the european energy policy

In the early years of the common European Energy policy, France has been labelled a black sheep or a bad pupil when it came to applying European and international energy, climate and energy market frameworks. As the country is currently engaged, in its own energy transition process (la transition énergétique) the question arises if these attributions are still valid. This article argues that several points of conflict between Paris and Brussels in the field of energy policy can still be identified today, especially in the areas of market liberalisation, the non-ETS sector and the promotion of renewable energy. Following the EU-compliance theory, the reasons for Paris’ deviations can be identified as both intentional and unintentional. On the one hand France is still politically reluctant to fully commit to Brussels energy policy objectives as they clash with the country’s view on the energy sector as a public welfare task. On the other hand, France faces systemic constraints due to its strong nuclear fleet, that dominates the country’s energy sector and therefore reduces Paris’ manoeuvrability within the European Energy framework. Because of this, France should be seen as an involuntary black sheep today, when it comes to non-compliance in EU-energy policy. Based on recent policy developments as well as energy production, consumption and GHG-emission data this article describes the current state of French and European energy and climate policies before analysing Paris’ performance towards the European framework goals as well as political reasons and structural constraints that inhibit further progress.