Energy models for demand forecasting—A review

Suganthi, L.; Samuel, Anand A.

doi:10.1016/j.rser.2011.08.014

Cited by 991 publications

(514 citation statements)

References 325 publications

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“…To forecast electricity demands in short term, medium term and long term periods, energy models are developed. [20] represents a review of various energy demand forecasting models, such as time series, regression, econometric, autoregressive integrated moving average, soft computing techniques, support vector regression, ant colony and particle swarm optimization. Energy consumption forecasts using artificial neural networks for Greece and Turkey are given in [21] and [22] respectively.…”

Section: Energy Consumption Growth Modellingmentioning

confidence: 99%

Small Biogas Plant Stability Prediction Model

2018

Teh. vjesn.

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Abstract:In this paper stability prediction for a small biogas plant in islanded mode depending on local consumers energy consumption growth is given. Biogas plant with two cogeneration facilities and local consumers is tested for on grid and islanded operation and thus represents a microgrid. Generator and regulator modelling as well as surrounding electrical grid modelling in software package Power World Simulator are described. Expected consumption growth through the period of 20 years in microgrid area is simulated. All generator important variables, i.e. voltage, frequency, real and reactive power in case of islanded mode are examined and explained. Finally, stability prediction analysis of biogas plant and surrounding consumers for 20 years period is given.

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Section: Energy Consumption Growth Modellingmentioning

confidence: 99%

Small Biogas Plant Stability Prediction Model

2018

Teh. vjesn.

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“…The activation function used for the input and hidden layers is the function and, the output layer makes use a linear or function. FFNN have been widely used for load forecasting with success [2,16,17] due their ease of application with inputs from different sources and good performance.…”

Section: Feedforward Neural Networkmentioning

confidence: 99%

“…If the current pattern of energy consumption continues, at a global level, the total world demand will increase by more than 50 % before 2030 [1]. Meanwhile, the high demand for energy and its production causes degradation of the environment as most energy resources are non-renewable [2]. Allied to the fact that big changes are currently happening to the in the utility industry due to deregulation and an increase in competition, policy makers and utilities are continuously seeking to identify ways to increase energy efficiency and alternate energy sources [3].…”

Section: Introductionmentioning

confidence: 99%

“…Several methods to model these relationships have been applied in the past such as regression, econometric, time series, decomposition, co-integration, ARIMA, artificial intelligence, fuzzy and support vector models [2,7]. In the work of Lin et al [8,9], a stock exchange index (TAIEX) was used in order to better the performance of short-term load forecasting (STLF) at times of global economic downturn.…”

Section: Introductionmentioning

confidence: 99%

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GA-ANN Short-Term Electricity Load Forecasting

Viegas

Vieira

Melício

et al. 2016

IFIP Advances in Information and Communication Technology

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“…The recent abundance of literatures focusing on the application of ANNs for predicting energy consumption of individual and complex buildings and their applications in load forecasting indicates the superior capability of ANNs for predictive modelling [25].…”

Section: Formulation Of the Proposed Modelmentioning

confidence: 99%

Generation of synthetic benchmark electrical load profiles using publicly available load and weather data

Pillai¹,

Putrus²,

Pearsall³

2014

International Journal of Electrical Power & Energy Systems

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Electrical load profiles of a particular region are usually required in order to study the performance of renewable energy technologies and the impact of different operational strategies on the power grid. Load profiles are generally constructed based on measurements and load research surveys which are capital and labour-intensive.In the absence of true load profiles, synthetically generated load profiles can be a viable alternative to be used as benchmarks for research or renewable energy investment planning. In this paper, the feasibility of using publicly available load and weather data to generate synthetic load profiles is investigated. An Artificial Neural Network (ANN) based method is proposed to synthesize load profiles for a target region using its Typical Meteorological Year 2 (TMY2) weather data as the input. To achieve this, the proposed ANN models are first trained using TMY2 weather data and load profile data of neighbouring regions as the input and targeted output. The limited number of data points in the load profile dataset and the consequent averaging of TMY2 weather data to match its period resulted in limited data availability for training. This challenge was tackled by incorporating generalization using Bayesian regularization into training. The other major challenge was facilitating ANN extrapolation and this was accomplished by the incorporation of domain knowledge into the input weather data for training. The performance of the proposed technique has been evaluated by simulation studies and tested on three real datasets. Results indicate that the generated synthetic load profiles closely resemble the real ones and therefore can be used as benchmarks.

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Energy models for demand forecasting—A review

Cited by 991 publications

References 325 publications

Small Biogas Plant Stability Prediction Model

Small Biogas Plant Stability Prediction Model

GA-ANN Short-Term Electricity Load Forecasting

Generation of synthetic benchmark electrical load profiles using publicly available load and weather data

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