Wind power forecasting using historical data and artificial neural networks modeling

One of the main parameters affecting the reliability of the renewable energy sources (RES) system, compared to the local conventional power station, is the ability to forecast the RES availability for a few hours ahead. To this end, the main objective of this work is the prognosis of the mean, maximum and minimum hourly wind power (WP) 8hours ahead. For this purpose, Artificial Neural Networks (ANN) modeling is applied. For the appropriate training of the developed ANN models hourly meteorological data are used. These data have been recorded by a meteorological mast in Tilos Island, Greece. For the evaluation of the developed ANN forecasting models proper statistical evaluation indices are used. According to the results, the coefficient of the determination ranges from 0.285 up to 0.768 (mean hourly WP), from 0.227 up to 0.798 (maximum hourly WP) and from 0.025 up to 0.398 (minimum hourly WP). Furthermore, the proposed forecasting methodology shows that is able to give sufficient and adequate prognosis of WP by a wind turbine in a specific location 8 hours ahead. This will be a useful tool for the operator of a RES system in order to achieve a better monitoring and a better management of the whole system

Development and application of artificial neural network models to estimate values of a complex human thermal comfort index associated with urban heat and cool island patterns using air temperature data from a standard meteorological station

© 2018, ISB. The present study deals with the development and application of artificial neural network models (ANNs) to estimate the values of a complex human thermal comfort-discomfort index associated with urban heat and cool island conditions inside various urban clusters using as only inputs air temperature data from a standard meteorological station. The index used in the study is the Physiologically Equivalent Temperature (PET) index which requires as inputs, among others, air temperature, relative humidity, wind speed, and radiation (short- and long-wave components). For the estimation of PET hourly values, ANN models were developed, appropriately trained, and tested. Model results are compared to values calculated by the PET index based on field monitoring data for various urban clusters (street, square, park, courtyard, and gallery) in the city of Athens (Greece) during an extreme hot weather summer period. For the evaluation of the predictive ability of the developed ANN models, several statistical evaluation indices were applied: the mean bias error, the root mean square error, the index of agreement, the coefficient of determination, the true predictive rate, the false alarm rate, and the Success Index. According to the results, it seems that ANNs present a remarkable ability to estimate hourly PET values within various urban clusters using only hourly values of air temperature. This is very important in cases where the human thermal comfort-discomfort conditions have to be analyzed and the only available parameter is air temperature

Assessing the Status of Electricity Generation in the Non-Interconnected Islands of the Aegean Sea Region

Assessment of the electricity generation status for Non-Interconnected Islands (NIIs) of the Aegean Sea region, excluding the electricity systems of Crete and Rhodes, is undertaken in the current study. The authors focus on the long-term analysis of thermal power generation characteristics and also on the challenges so far limiting the contribution of Renewable Energy Sources (RES) in covering the electricity needs of the specific area. According to the present analysis, due to the existing technical limitations, the annual RES shares in the electricity balance of NIIs of the Aegean Sea have since 2010 stagnated in the range of 15% to 18%. Moreover, the performance of thermal power stations for all 30 NII systems is evaluated on the basis of their utilization factor, associated fuel consumption and electricity production costs. The vast majority of these stations is characterized by low capacity factors in combination with high specific fuel consumption and high operational expenses that in the case of smaller scale island regions could even exceed 600€/MWh. At the same time, the authors discuss on the alternatives and encourage further investigation of novel, intelligent energy solutions, such as the smart microgrid and battery-based hybrid power station that are currently developed on the island of Tilos under the implementation of the TILOS Horizon 2020 program

Transportation and air quality perspectives and projections in a Mediterranean country, the case of Greece

This study provides a thorough review and analysis of the evolution of the Greek vehicle fleet over the last ~30 years, which is next used for the generation of high granularity fleet projections, the assessment of associated air pollution and the estimation of relevant environmental benefits by 2030. The integrated methodology developed takes also into account vehicle clustering and the Brown’s Double Simple Exponential Smoothing technique that, together with the adoption of COPERT-based emission factors, allow for the estimation of the anticipated emissions in 2030. Expected 2030 emissions levels suggest a reduction across all pollutants compared to 2018, ranging from 3.7% for PM10 to 54.5% for NMVOC (and 46% for CO, 14% for SO2, 28% for NOX and 21% for CO2). We find that Greece is on track with national goals concerning the reduction of air pollution from the transportation sector, which designates the positive contribution anticipated by EVs and new, “greener” vehicles, and sets new challenges for the further improvement of the sector beyond the 2030 outlook

Estimation of Particulate Matter Impact on Human Health within the Urban Environment of Athens City, Greece

The main objective of this work is the assessment of the annual number of hospital admissions for respiratory diseases (HARD) due to the exposure to inhalable particulate matter (PM10), within the greater Athens area (GAA), Greece. To achieve this aim, on the one hand, time series of the particulate matter with aerodynamic diameter less than 10 μm (PM10) recorded in six monitoring stations located in the GAA, for a 13-year period 2001–2013, have been statistically analyzed. On the other hand, the AirQ2.2.3 software developed by the World Health Organization (WHO) was used to evaluate adverse health effects by PM10 in the GAA during the examined period. The results show that, during the examined period, PM10 concentrations present a significant decreasing trend. Also, the mean annual HARD cases per 100,000 inhabitants ranged between 20 (suburban area) and 40 (city center area). Approximately 70% of the annual HARD cases are due to city center residents. In all examined sites, a declining trend in the annual number of HARD cases appears. Moreover, a strong relation between the annual number of HARD cases and the annual number of days exceeding the European Union daily PM10 threshold value was found

Day-Ahead Forecasting of the Theoretical and Actual Wind Power Generation in Energy-Constrained Island Systems

Grid operators of islands with limited system tolerance are often challenged by the need to curtail wind energy in order to maintain system stability and security of supply. At the same time, and in the absence of storage facilities and/or other means of flexibility such as demand-side management, wind park owners face the problem of rejected wind energy production that varies considerably within the year. In the prospect of a more dynamic market operation in island grids, estimation of the anticipated wind energy curtailments may allow the evaluation of different options for wind park owners, such as short-term leasing of energy storage and/or direct, bilateral power purchase agreements with flexible demand entities. To enable such options, effective wind energy forecasting is necessary not only in terms of theoretical production, but also in terms of actual production being absorbed by the system. In this direction, the current research works on the prediction of day-ahead wind energy production in island grids, aiming to generate both theoretical (expected) and actual wind power forecasts. To that end, we use artificial neural networks for the development of different day-ahead forecasting models of hourly granularity, and we then test their performance in a large-scale non-interconnected island system, where annual wind energy curtailments for local wind parks may exceed 25% of the respective theoretical yield. Our results indicate that models developed provide a fair accuracy of day-ahead wind energy predictions, which is further elaborated by initiating a discussion on the emergence of alternative actor schemes in similar systems