... inputs and outputs each one and this allows all the units produced to be analyzed in greater depth (Färe and Grosskopf, 2000). © Emerald Publishing Limited 2022 Emerald Publishing Limited Licensed re-use rights only Efficiency Network DEA Oil industry Energy sector Modeling Neural...

... 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...

... works from that area in the following. Artificial intelligence Forecasting Neural networks Electricity Participating in electricity markets requires decision support through accurate forecasts of future prices (Nogales et al., 2002 ; Weron, 2014). This is relevant for various...

...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...

... model Energy sector Energy production Neural networks Thermal power Operation management Evolutionary algorithms Output power forecasting is an important activity in power plant utilization (Li et al., 2016). Output power forecasting studies changes and fluctuations trend...

... 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...

... and elaborated the work on widely used different methods such as artificial neural networks and support vector machine (SVMs). (Kwac et al., 2014) proposed the segmentation method for energy consumption using hourly data of households by taking the load shapes of the energy consumed in different hours...

... in loss reduction. Load at each hour of the day needs to be forecasted to estimate the LMP at each DG bus. A two-layer artificial neural network has been used to forecast the load. The original contributions of this paper are as follows: Fair allocation of reduced losses among DG units...

...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...