Insular power systems are characterized by their isolated geographical location, which makes their interconnection with other power systems a challenging task. Moreover, these islands have important renewable resources that allow the reduction of generation costs and greenhouse gas emissions (GHE). To guaranty the quality, flexibility and robustness of the electrical framework, the representati...

This paper proposes a short-term load forecasting method for natural gas using deep learning. Deep learning has proven to be a powerful tool for many classification problems seeing significant use in machine learning fields like image recognition and speech processing. This paper explores many aspects of using deep neural networks for time series forecasting. It is determined that the proposed ...

In this study, the energy consumption in the food and beverage industries of Iran was investigated. The energy consumption in this sector was modeled using artificial neural network (ANN), response surface methodology (RSM) and genetic algorithm (GA). First, the input data to the model were calculated according to the statistical source, balance-sheets and the method proposed in this paper. It ...

Introduction Forecasting demand is both a science and an art. Econometric methods of forecasting, in the context of energy demand forecasting, can be described as ‘the science and art of specification, estimation, testing and evaluation of models of economic processes’ that drive the demand for fuels. The need and relevance of forecasting demand for an electric utility has become a much-discuss...

-The load forecasting is required in power system management and ensures electricity providing for customers. Photovoltaic power forecasting aims to reduce the fuel consumption and play important role in the supervisory control for a hybrid energy system. This paper presents the application of new model using neural networks (NN) and Particle Swarm Optimization (PSO) to determine the net load f...