Load forecasting
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Part 2 book "The electric power engineering handbook - Power systems" includes content: Planning environments, short term load and price forecasting with artificial neural networks - transmission plan evaluation, assessment of system reliability, power system planning, power system reliability, probabilistic methods for planning and operational analysis, engineering principles of electricity pricing, business essentials,... and other contents.
299p dianmotminh03 17-06-2024 0 0 Download
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Part 1 book "Electric power system planning - Issues, algorithms and solutions" includes content: Power system planning, basic principles; optimization techniques; some economic principles; load forecasting; single bus generation expansion planning; multi bus generation expansion planning; substation expansion planning.
147p dianmotminh01 20-05-2024 5 4 Download
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Part 1 book "Electric power systems - Advanced forecasting techniques and optimal generation scheduling" includes content: Overview of electric power generation systems, uncertainty and risk in generation scheduling, short term load forecasting, short term electricity price forecasting, short term wind power forecasting.
193p dianmotminh01 20-05-2024 4 2 Download
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The novelty that this paper identifies relates to the reduction of cost volatility in the global supply chain. This novelty is further seen in the proposition that blockchain can achieve this objective. Blockchain’s distributed ledger framework achieves improved inventory control, improved demand forecasting, and more efficient just-in-time productivity as factors to reduce cost-loading in the global supply chain.
10p longtimenosee06 27-03-2024 3 2 Download
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In this paper, a short-term load forecasting model is proposed based on Long Short-Term Memory (LSTM) network combining linear regression algorithm and EVN-NLDC (National Load Dispatch Centre) data. Short-term load forecasting has an enormous impact on unit commitment, strategic power reserve for national power system and enhances the reliability of the power supply.
4p vijeff 01-12-2023 7 4 Download
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This paper is aimed to apply hybrid machine learning model namely GA-ANFIS, which is a combination of Adaptive Neuro-Fuzzy Inference System (ANFIS) and Genetic Algorithm (GA), for the prediction of total bearing capability of driven piles.
8p visharma 20-10-2023 8 4 Download
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This paper presents a short-term load forecasting model using the backpropagation neural network (BPNN) model. The proposed model is based on data on loads and factors that directly affect electricity demand, such as temperature, humidity, load over time in the past, etc., collected from the electricity market ISO New England.
8p viisac 15-09-2023 8 3 Download
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Short-term load forecasting of buildings based on artificial neural network and clustering technique
In this paper, we propose an artificial neural network (ANN) based method to predict the energy use in campus buildings in short-term time series from one hour up to one week. The proposed method analyzes and extracts the features from the historical data of load and temperature to generate the prediction of future energy consumption in the building based on sparsified K-means. To evaluate the performance of the proposed approach, historical load data in hourly resolution collected from the campus buildings in Chonnam National University were used.
13p nhanchienthien 25-07-2023 8 4 Download
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Power load forecasting is an important issue in microgrid energy management. Accurate load forecasting is urgently required for effective power management for microgrid. This paper considers the evaluation of the effectiveness of applying different optimization algorithms to the proposed Deep Learning Neural Network, which is Wavenet.
9p viwolverine 07-07-2023 5 2 Download
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Biogas energy is considered a renewable energy source. The efficient usage of biogas resources can help reduce greenhouse gas emission, especially methane, generate electricity to power farms’ loads, and decrease load demand on grids.
6p vifalcon 16-05-2023 8 4 Download
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The results show that there are 8 load paterns in the past from 2006. In which, load paterns in years of 2010, 2012, and 2014 have same shapes or “rules”. They are used to forcaste load profile of those paterns to 2030.
7p vidoctorstrange 06-05-2023 5 2 Download
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The building sector is a significant energy consumer, and its share of energy consumption is increasing because of urbanization. Forecasting the electricity load for improving building energy efficiency is imperative for reducing energy costs and environmental impacts.
5p vifred 23-12-2022 13 3 Download
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This study presents an effective method based on long short-term memory to reduce the computational cost in nonlinear static analysis of functionally graded plates. Data points representing a load-deflection curve in a dataset are generated through isogeometric analysis.
17p vimclaren 12-10-2022 3 2 Download
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This paper proposes a bi-level optimization model to maximize the net revenue of a power distribution company owning energy storage systems. Furthermore, the proposed model also takes into account the uncertainty of load forecast based on a set of multiple scenarios in order to assess its impact on the maximum net revenue of the power distribution company.
8p vigeneralmotors 13-07-2022 12 6 Download
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Electrical energy is fundamental to the life and economic development. Accompany by the increasing demand for electricity by people and businesses, the electricity industry must always ensure sufficient supply for production activities. This poses an urgent need to have an accurate load forecasting tool for electricity production and distribution strategy.
6p vigeneralmotors 13-07-2022 529 5 Download
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The paper "Improving planning reliability and project performance using the rational commitment model" proposes the Rational Commitment Model (RCM), a new decision decision-making tool based on lean principles, which uses statistical models to obtain more reliable commitment planning at operational level. RCM allows forecasting commitment planning for short term-periods using information about workers, buffers, and planned progress.
33p runordie3 27-06-2022 17 6 Download
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This study proposes an alternative approach based on the deep learning paradigm working in a complementary way with conventional methods such as the finite element method for quickly forecasting the responses of structures under random wind loads with reasonable accuracy. The approach works in a sequenceto-sequence fashion, providing a good trade-off between the prediction performance and required computation resources.
11p haoasakura 30-05-2022 32 2 Download
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Load forecasting has always been a crucial part of an efficient power system planning an operation. Since the global electricity market has developed rapidly in recent years, accurate load forecasting is becoming increasingly difficult. ShortTerm Load Forecasting (STLF) has an enormous impact on unit commitment, strategic power reserve for national power systems and enhances the power supply’s reliability.
4p vistephenhawking 26-04-2022 17 2 Download
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Unit commitment is a critical problem in electrical power system optimization that has been well solved over the years. This problem is more complicated for microgrids with high penetration of renewable energy resources. In that context, stochastic optimization has emerged as a remarkable approach that is considered uncertainties in the unit commitment problem besides robust optimization.
4p vistephenhawking 26-04-2022 12 2 Download
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Ultra-short-term load forecasting is one of the core techniques for distribution networks operation. Actually, combined forecasting solution is usually used with strong randomness of loads. So how to weight the different forecasting model is a challenge.
4p viericschmid 12-01-2022 18 0 Download