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1. Hybrid Deep Learning Applied on Saudi Smart Grids for Short-Term Load Forecasting.

Author

Alrasheedi, Abdullah and Almalaq, Abdulaziz

Subjects
LOAD forecasting (Electric power systems), DEEP learning, ARTIFICIAL neural networks, RECURRENT neural networks, FORECASTING, ELECTRIC power distribution grids, ARTIFICIAL intelligence
Abstract

Despite advancements in smart grid (SG) technology, effective load forecasting utilizing big data or large-scale datasets remains a complex task for energy management, planning, and control. The Saudi SGs, in alignment with the Saudi Vision 2030, have been envisioned as future electrical grids with a bidirectional flow of power and data. To that end, data analysis and predictive models can enhance Saudi SG planning and control via artificial intelligence (AI). Recently, many AI methods including deep learning (DL) algorithms for SG applications have been published in the literature and have shown superior time series predictions compared with conventional prediction models. Current load-prediction research for the Saudi grid focuses on identifying anticipated loads and consumptions, on utilizing limited historical data and the behavior of the load's consumption, and on conducting shallow forecasting models. However, little scientific proof on complex DL models or real-life application has been conducted by researchers; few articles have studied sophisticated large-scale prediction models for Saudi grids. This paper proposes hybrid DL methods to enhance the outcomes in Saudi SG load forecasting, to improve problem-relevant features, and to accurately predict complicated power consumption, with the goal of developing reliable forecasting models and of obtaining knowledge of the relationships between the various features and attributes in the Saudi SGs. The model in this paper utilizes a real dataset from the Jeddah and Medinah grids in Saudi Arabia for a full year, 2021, with a one-hour time resolution. A benchmark strategy using different conventional DL methods including artificial neural network, recurrent neural network (RNN), conventional neural networks (CNN), long short-term memory (LSTM), gated recurrent unit (GRU), and different real datasets is used to verify the proposed models. The prediction results demonstrate the effectiveness of the proposed hybrid DL models, with CNN–GRU and CNN–RNN with NRMSE obtaining 1.4673% and 1.222% improvements, respectively, in load forecasting accuracy. [ABSTRACT FROM AUTHOR]

Published
2022
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2. Improved Medium Term Approach for Load Forecasting of Nigerian Electricity Network Using Artificial Neuro-Fuzzy Inference System: A Case Study of University of Nigeria, Nsukka.

Author

Eya, Candidus.U., Salau, Ayodeji Olalekan, Braide, Sepiribo Lucky, and Chigozirim, Onwe Divine

Subjects
ELECTRIC power consumption, LOAD forecasting (Electric power systems), ELECTRIC power systems, ARTIFICIAL intelligence, ENERGY consumption, FORECASTING
Abstract

The demand for electrical energy continues to rise at an alarming rate, while supply is not proportional to power demand. Thus, in order to accomplish appropriate preparation and operation of an electrical power system network, it is crucial to properly assess the present and future consumption of electric power systems. Meeting the electric load needs entails actively anticipating the amount of energy required to meet the subscribers' power demand. To forecast energy demand, many algebraic and artificial intelligence schemes have been used. This paper proposes an improved medium-term forecasting scheme that employs an artificial neural network (ANN). The model was developed and trained with the help of an artificial neuro-fuzzy inference system (ANFIS). The data used, which ranged from 2014 to 2019, was obtained from the University of Nigeria Nsukka's (UNN) research and energy Centre, and the works department of the same university. The model was designed and simulated using MATLAB 2018a, and the results were evaluated using MAPE. The proposed system used a robust machine learning based scheme. Unlike conventional systems, the system demonstrated a MAPE of 4.34% and a correlation of 0.91144315 at predicted and actual loads, as shown in Table 2. The proposed system shows that the predicted loads has 25th and 75th percentiles of 0.58MW and 0.98WM, while that of the actual loads are 0.649MW and 0.936MW. [ABSTRACT FROM AUTHOR]

Published
2023
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3. Fuzzy Wavelet Neural Networks for City Electric Energy Consumption Forecasting

Author

Hui Wang and Ping Zhang

Subjects
Consumption (economics), Mathematical optimization, Engineering, Artificial neural network, business.industry, forecasting, Energy consumption, Machine learning, computer.software_genre, Fuzzy logic, Term (time), Wavelet, Energy(all), Electric energy consumption, Artificial intelligence, Electricity, electric energy consumptin, nonlinear sequence, fuzzy wavelet neural networks, business, computer
Abstract

In view of the defects of the prediction model based on neural network, such as when doing prediction of nonlinear sequence, it is likely to fall into local hypo-strong point, and the rate of training is very slow. This paper presents a fuzzy wavelet neural network (FWNN) approach for annual electricity consumption in high energy consumption city. It is claimed that, due to high fluctuations of energy consumption in high energy consumption cities, conventional regression models do not forecast energy consumption correctly and precisely. Although ANNs have been typically used to forecast short term consumptions, this paper shows that it is a more precise approach to forecast annual electricity consumption. Furthermore, the FWNN approach based on ANN is used to show it can estimate the annual consumption with less error. Actual data from high energy consuming (intensive) from 1983 to 2003 is used to illustrate the applicability of the FWNN approach. This is the first study to present an algorithm based on the ANN and wavelet for forecasting long term electricity consumption in high energy consuming city. The prediction effect of wavelet neural network prediction model is proved in matlab7.0 simulation environment. A better prediction result is gained, and the defect of falling into local hypo-strongpoint is overcome, at the same time, the rate of training is raised compared with the prediction model based on artificial neural network. The calculation result shows that the presented model is effective.

Published
2012
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4. Evaluation of Deep Learning Methods in a Dual Prediction Scheme to Reduce Transmission Data in a WSN

Author

Fernando Rangel de Sousa, Carlos R. Morales, Nestor C. Fernandes, and Valner Joao Brusamarello

Subjects
Computer science, Real-time computing, Redes sem fio, forecasting, TP1-1185, Biochemistry, Article, Analytical Chemistry, Reduction (complexity), Base station, energy saving, Energy saving, Electrical and Electronic Engineering, Representation (mathematics), wireless sensor networks, Instrumentation, Redes neurais, Artificial neural network, business.industry, Deep learning, Chemical technology, Modelos de previsão, Energy consumption, Aprendizado de máquina, neural networks, Atomic and Molecular Physics, and Optics, Wireless sensor networks, Transmission (telecommunications), Artificial intelligence, business, Wireless sensor network, Neural networks, Forecasting
Abstract

One of the most important challenges in Wireless Sensor Networks (WSN) is the extension of the sensors lifetime, which are battery-powered devices, through a reduction in energy consumption. Using data prediction to decrease the amount of transmitted data is one of the approaches to solve this problem. This paper provides a comparison of deep learning methods in a dual prediction scheme to reduce transmission. The structures of the models are presented along with their parameters. A comparison of the models is provided using different performance metrics, together with the percent of points transmitted per threshold, and the errors between the final data received by Base Station (BS) and the measured values. The results show that the model with better performance in the dataset was the model with Attention, saving a considerable amount of data in transmission and still maintaining a good representation of the measured data.

Published
2021

5. Energy consumption forecasting using genetic fuzzy rule-based systems based on MOGUL learning methodology

Author

Isabel Praça, Zita Vale, Tiago Pinto, Brígida Teixeira, Francisco Silva, Aria Jozi, and Juan Manuel Corchado

Subjects
Consumption (economics), Mathematical optimization, Fuzzy rule, Artificial neural network, MOGUL, Computer science, business.industry, 020209 energy, Fuzzy rule based methods, 02 engineering and technology, Energy consumption, Electricity consumption, Forecasting, Fuzzy logic, Standard deviation, 0202 electrical engineering, electronic engineering, information engineering, Electricity, Artificial intelligence, business, Cost of electricity by source
Abstract

One of the most challenging tasks for energy domain stakeholders is to have a better preview of the electricity consumption. Having a more trustable expectation of electricity consumption can help minimizing the cost of electricity and also enable a better control on the electricity tariff. This paper presents a study using a Methodology to Obtain Genetic fuzzy rule-based systems Under the iterative rule Learning approach (MOGUL) methodology in order to have a better profile of the electricity consumption of the following hours. The proposed approach uses the electricity consumption of the past hours to forecast the consumption value for the following hours. Results from this study are compared to those of previous approaches, namely two fuzzy based systems: and several different approaches based on artificial neural networks. The comparison of the achieved results with those achieved by the previous approaches shows that this approach can calculate a more reliable value for the electricity consumption in the following hours, as it is able to achieve lower forecasting errors, and a less standard deviation of the forecasting error results.

Published
2017