... industry Energy sector Modeling Neural networks Dynamic programming Crude oil C14 C61 C67 Q49 The oil sector has been an important part of the national economy, essentially as a complement to industrial economic growth because of the fact that energy is strongly related to almost...

... forecasting Autoregressive Neural networks Fuzzy-logic model Demand-side management Gasoline Liquid fuels Fuel demand Forecasting methods Time series Machine learning Forecast evaluation The management of energy demand has become a mandatory issue for public and private agents working...

... in the West region of Cameroon. Two well-known artificial neural networks, namely, multi-layer perceptron (MLP) and nonlinear autoregressive network with exogenous inputs (NARX), were used to model the wind speed profile of the city of Bapouh in the West-region of Cameroon. Design/methodology/approach...

... setting. Based on this, Section 5 evaluates the sensitivity of the overall forecasting accuracy to different predictors. Section 6 discusses managerial and policy implications, followed by concluding remarks. Artificial intelligence Forecasting Neural networks Electricity Participating...

...Emmanuel Bannor B.; Alex O. Acheampong Purpose This paper aims to use artificial neural networks to develop models for forecasting energy demand for Australia, China, France, India and the USA. Design/methodology/approach The study used quarterly data that span over the period of 1980Q1-2015Q4...

... (data set: 2,012 days). Multivariate linear regression (MLR) and multi-layer perceptron artificial neural network (ANN) methods are separately used to anticipate the energy consumption. The baseline will be assumed as a reference to be compared with the actual data to estimate the real saving values...

... analysis (Lee and Hong, 2015), artificial neural networks (ANNs) (Hernández et al., 2014), similar-day approaches (Demiroren and Ceylan,2006), support vector machine (Che and Wang, 2014), combination of neural network and wavelet theory (Deihimi et al., 2013), combination of neural...

... objective function. The proposed method also considers wind speed probability factor via PSO-artificial neural network (ANN) technique and hydro power generation at peak load demand condition to ensure economic utilization. Originality/value To validate the advantage of the proposed approach, six...

... Correlation analysis Forecasting Neural networks Electricity There has been an increase in the usage of electronics appliances in home. Studies show that increase in the “home appliances” is accepted to grow by 15.3 per cent of annual growth rate, resulting in a market volume of US$2,227m in 2022...

... at: ch.venkaiah@ieee.org © Emerald Publishing Limited 2018 Emerald Publishing Limited Licensed re-use rights only Artificial intelligence Simulation Pricing Forecasting Neural networks Distribution Cost comparison Restructuring ( Δ P l o s s ) b i = Change...

...Isham Alzoubi; Mahmoud Delavar; Farhad Mirzaei; Babak Nadjar Arrabi Purpose This work aims to determine the best linear model using an artificial neural network (ANN) with the imperialist competitive algorithm (ICA-ANN) and ANN to predict the energy consumption for land leveling. Design...

... distribution assumptions. The electricity prices on the deregulated market fall into this category. Design/methodology/approach The paper presents alternative approaches, i.e. memory-based prediction and fractal approach compared with established nonlinear method of neural networks. The appropriate...