Abstract—With the increasing penetration of solar power into power systems, forecasting becomes critical in power system operations. In this paper, an hourly-similarity (HS) based method is developed for 1-hour-ahead (1HA) global horizontal irradiance (GHI) forecasting. This developed method utilizes diurnal patterns, statistical distinctions between different hours, and hourly similarities in ...

Generation and load balance is required in the economic scheduling of generating units in the smart grid. Variable energy generations particularly from wind and solar energy resources are witnessing a rapid boost, and, it is anticipated that with a certain level of their penetration, they become noteworthy sources of uncertainty. As in the case of load demand, energy forecasting can also be use...

The day-ahead electricity market is closely related to other commodity markets such as the fuel and emission markets and is increasingly playing a significant role in human life. Thus, in the electricity markets, accurate electricity price forecasting plays significant role for power producers and consumers. Although many studies developing and proposing highly accurate forecasting models exist...

A sustainable production of electricity is essential for low carbon green growth in South Korea. The generation of wind power as renewable energy has been rapidly growing around the world. Undoubtedly, wind energy is unlimited in potential. However, due to its own intermittency and volatility, there are difficulties in the effective harvesting of wind energy and the integration of wind power in...

Age-specific mortality rates are often disaggregated by different attributes, such as sex, state and ethnicity. Forecasting age-specific mortality rates at the national and sub-national levels plays an important role in developing social policy. However, independent forecasts of agespecific mortality rates at the sub-national levels may not add up to the forecasts at the national level. To addr...

This work presents a time series forecasting method based on Long Short-Term Memory (LSTM) network, which can be utilized for macroeconomic variable forecasting, like Gross Domestic Product. LSTM is popular in Artificial Neural Networks and an active research topic, however applications are limited. The current focuses one-step ahead forecast, uses Python Keras libraries the implementation. app...

To improve the prediction accuracy of short-term load series, this paper proposes a hybrid model based on multi-trait-driven methodology and secondary decomposition. In detail, four steps were performed sequentially, i.e., data decomposition, individual prediction, ensemble output, all which designed methodology. particular, multi-period identification method judgment basis decomposition to ass...

Time Series Forecasting (TSF) is an important tool to support decision making (e.g., planning production resources). Artificial Neural Networks (ANN) are innate candidates for TSF due to advantages such as nonlinear learning and noise tolerance. However, the search for the best model is a complex task that highly affects the forecasting performance. In this work, we propose two novel Evolutiona...

drought is random and nonlinear phenomenon and using linear stochastic models, nonlinear artificial neural network and hybrid models is advantaged for drought forecasting. this paper presents the performances of autoregressive integrated moving average (arima), direct multi-step neural network (dmsnn), recursive multi-step neural network (rmsnn), hybrid stochastic neural network of directive ap...

We present a winning method of the IEEE DataPort Competition on Day-Ahead Electricity Demand Forecasting: Post-COVID Paradigm. The day-ahead load forecasting approach is based novel online forecast combination multiple point prediction models. It contains four steps: i) data cleaning and preprocessing, ii) new holiday adjustment procedure, iii) training individual models, iv) by smoothed Bernst...