Abstract This study suggests an ultra-short-term photovoltaic (PV) energy generation forecasting model based on Seasonal Autoregressive Integrated Moving Average (SARIMA) and Support Vector Machine (SVM). It can further increase the predictive performance of PV electricity electrical output address issue significant fluctuation instability power power. To achieve combined prediction, SVM is emp...

Now-a-days, different sectors of the economy are being significantly affected by the electricity variable. In this research, we analyzed the monthly electricity consumption in Pakistan for the period of January 1990 through December 2011, using linear and non linear modeling techniques. They include ARIMA, Seasonal ARIMA (SARIMA) and ARCH/GARCH models. Electricity consumption model reveals a si...

In this study we examined the impact of weather variability and tides on the transmission of Barmah Forest virus (BFV) disease and developed a weather-based forecasting model for BFV disease in the Gladstone region, Australia. We used seasonal autoregressive integrated moving-average (SARIMA) models to determine the contribution of weather variables to BFV transmission after the time-series dat...

در این پژوهش سعی می‌­شود مدلی برای شبیه‌­سازی چرخه عمر صنعت برق با استفاده از شبیه‌سازی عامل­بنیان ارائه شود، مدل، 5 عامل استخراج شد و شبیه­‌سازی به کمک نرم‌افزار Anylogic صورت پذیرفت. بهینه‌­سازی مدل چهار سناریو نظر خبرگان شد. سناریوی نخست، جذابیت نیروگاه گازی سیکل ترکیبی کاهش یافت بر دو دیگر افزوده نتیجه افزایش تولید آبی است که توجه کمبود منابع کشور به‌صرفه نیست. دوم ورود یک فناوری جدید میزان...

Abstract The prediction of river runoff is crucial for flood forecasting, agricultural irrigation and hydroelectric power generation. A coupled model based on the Gravitational Search Algorithm (GSA) Seasonal Autoregressive Integrated Moving Average (SARIMA) proposed to address non-linear seasonal features data. GSA has a significant local optimisation capability, while SARIMA allows real-time ...