Forecasting methods in energy planning models
KB Debnath, M Mourshed - Renewable and Sustainable Energy Reviews, 2018 - Elsevier
Energy planning models (EPMs) play an indispensable role in policy formulation and energy
sector development. The forecasting of energy demand and supply is at the heart of an EPM …
sector development. The forecasting of energy demand and supply is at the heart of an EPM …
A review of electricity demand forecasting in low and middle income countries: The demand determinants and horizons
With the globally increasing electricity demand, its related uncertainties are on the rise as
well. Therefore, a deeper insight of load forecasting techniques for projecting future …
well. Therefore, a deeper insight of load forecasting techniques for projecting future …
Short-term electricity price and load forecasting in isolated power grids based on composite neural network and gravitational search optimization algorithm
Electricity price forecasting is a key aspect for market participants to maximize their
economic efficiency in deregulated markets. Nevertheless, due to its non-linearity and non …
economic efficiency in deregulated markets. Nevertheless, due to its non-linearity and non …
Neural network based optimization approach for energy demand prediction in smart grid
Energy usage and demand forecasting is an essential and complex task in real time
implementation. Proper coordination is required between the consumer and power …
implementation. Proper coordination is required between the consumer and power …
Day-ahead electricity price forecasting via the application of artificial neural network based models
IP Panapakidis, AS Dagoumas - Applied Energy, 2016 - Elsevier
Traditionally, short-term electricity price forecasting has been essential for utilities and
generation companies. However, the deregulation of electricity markets created a …
generation companies. However, the deregulation of electricity markets created a …
[HTML][HTML] Efficient daily electricity demand prediction with hybrid deep-learning multi-algorithm approach
Predicting electricity demand (G) is crucial for electricity grid operation and management. In
order to make reliable predictions, model inputs must be analyzed for predictive features …
order to make reliable predictions, model inputs must be analyzed for predictive features …
[HTML][HTML] Current status, challenges, and prospects of data-driven urban energy modeling: A review of machine learning methods
Urban energy modeling is essential in planning electricity generation and efficiently
managing electric power systems. Various urban energy models were developed for several …
managing electric power systems. Various urban energy models were developed for several …
Using PSO-GA algorithm for training artificial neural network to forecast solar space heating system parameters
B Jamali, M Rasekh, F Jamadi, R Gandomkar… - Applied Thermal …, 2019 - Elsevier
Abstract An Artificial Neural Network (ANN) model based on PSO-GA optimization algorithm
is applied to predict a Solar Space Heating System (SSHS) performance. An experimental …
is applied to predict a Solar Space Heating System (SSHS) performance. An experimental …
Cascade hydropower plants operation considering comprehensive ecological water demands
H Zhang, J Chang, C Gao, H Wu, Y Wang, K Lei… - Energy Conversion and …, 2019 - Elsevier
Hydropower plants operation may change river flow, thereby degrading the stability of river
ecosystems. The primary purpose of this paper is to compute the comprehensive ecological …
ecosystems. The primary purpose of this paper is to compute the comprehensive ecological …
[HTML][HTML] Pakistan's electrical energy crises, a way forward towards 50% of sustain clean and green electricity generation
Electrical sustainability is a foundation for urbanization and industrialization. Over the past
three decades, Pakistan has been convulsed by electricity shortages that at times have …
three decades, Pakistan has been convulsed by electricity shortages that at times have …