PI-controlled ANN-based Energy Consumption Forecasting for Smart Grids

Gezer, Gülsüm; Tuna, Gürkan; Kogias, Dimitrios G.; Gülez, Kayhan; Güngör, Vehbi Çağrı

doi:10.5220/0005516801100116

Cited by 8 publications

(5 citation statements)

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“…There is literature available that discusses heterogeneous sensing devices. These studies employ two types of forwarder selection strategies: proactive [30][31][32][33][34] and reactive [35][36][37][38]. In addition, [37][38][39][40] considers load distribution when selecting from a wide array of possible forwarders.…”

Section: A Fusion Iot Network For Forwarder Selectionmentioning

confidence: 99%

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Intelligent Energy Management with IoT Framework in Smart Cities Using Intelligent Analysis: An Application of Machine Learning Methods for Complex Networks and Systems

Nikpour,

Yousefi,

Jafarzadeh

et al. 2023

Preprint

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Smart buildings are increasingly using Internet of Things (IoT)-based wireless sensing systems to reduce their energy consumption and environmental impact. As a result of their compact size and ability to sense, measure, and compute all electrical properties, Internet of Things devices have become increasingly important in our society. A major contribution of this study is the development of a comprehensive IoT-based framework for smart city energy management, incorporating multiple components of IoT architecture and framework. An IoT framework for intelligent energy management applications that employ intelligent analysis is an essential system component that collects and stores information. Additionally, it serves as a platform for the development of applications by other companies. Furthermore, we have studied intelligent energy management solutions based on intelligent mechanisms. The depletion of energy resources and the increase in energy demand have led to an increase in energy consumption and building maintenance. The data collected is used to monitor, control, and enhance the efficiency of the system.

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Section: A Fusion Iot Network For Forwarder Selectionmentioning

confidence: 99%

“…The disadvantage is that power is expensive. Real-time data sets are applied to a traditional regression setup in the previous decade [32], [33], and then forecasts are recorded. Following this, neural networks are used to predict, which is also described in [34].…”

Section: A Fusion Iot Network For Forwarder Selectionmentioning

confidence: 99%

Intelligent Energy Management with IoT Framework in Smart Cities Using Intelligent Analysis: An Application of Machine Learning Methods for Complex Networks and Systems

Nikpour,

Yousefi,

Jafarzadeh

et al. 2023

Preprint

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“…In addition, in [19], SVMs and ANN-based models are reviewed for household energy consumption prediction. In a more specific study [20], an ANNbased forecasting tool is implemented to predict energy forecasts in a building. Here, nine different scenarios are considered, and different ANNs are developed according to a scenario.…”

Section: Energy Consumption Forecasting Techniquesmentioning

confidence: 99%

PSO Based Optimized Ensemble Learning and Feature Selection Approach for Efficient Energy Forecast

et al. 2021

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Swarm intelligence techniques with incredible success rates are broadly used for various irregular and interdisciplinary topics. However, their impact on ensemble models is considerably unexplored. This study proposes an optimized-ensemble model integrated for smart home energy consumption management based on ensemble learning and particle swarm optimization (PSO). The proposed model exploits PSO in two distinct ways; first, PSO-based feature selection is performed to select the essential features from the raw dataset. Secondly, with larger datasets and comprehensive range problems, it can become a cumbersome task to tune hyper-parameters in a trial-and-error manner manually. Therefore, PSO was used as an optimization technique to fine-tune hyper-parameters of the selected ensemble model. A hybrid ensemble model is built by using combinations of five different baseline models. Hyper-parameters of each combination model were optimized using PSO followed by training on different random samples. We compared our proposed model with our previously proposed ANN-PSO model and a few other state-of-the-art models. The results show that optimized-ensemble learning models outperform individual models and the ANN-PSO model by minimizing RMSE to 6.05 from 9.63 and increasing the prediction accuracy by 95.6%. Moreover, our results show that random sampling can help improve prediction results compared to the ANN-PSO model from 92.3% to around 96%.

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“…In ANNs, a large number of neurons are interconnected in a highly complex, nonlinear and massive parallel network. An ANN with an input layer, one or more hidden layers and one output layer is known as multilayer receptor (MLR) [9]. Each layer consists of several neurons, and each neuron is attached to an adjacent layer with synaptic weights.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Forecasting of Load and Renewable Energy Using Artifical Neural Network

Srinivasan¹,

Balasubramanian²,

Selvaraj³

2021

IJETT

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Load forecasting is a technique used for the prediction of electrical load demands in battery management. In general, the aggregated level used for Short-Term Electrical Load Forecasting (STLF) consists of either numerical or non-numerical information collected from multiple sources, which helps in obtaining accurate data and efficient forecasting. However, the aggregated level cannot precisely forecast the validation and testing phases of numerical data, including the real-time measurements of irradiance level (W/m 2 ) and photovoltaic output power (W). Forecasting is also a challenge due to the fluctuations caused by the random usage of appliances in the existing weekly, diurnal, and annual cycle load data. In this study, we have overcome this challenge by using Artificial Neural Network (ANN) methods such as Bayesian Regularization (BR) and Levenberg-Marquardt (LM) algorithms. The STLF achieved by ANN-based methods can improve the forecast accuracy. The overall performance of the BR and LM algorithms were analyzed during the development phases of the ANN. The input layer, hidden layer and output layer used to train and test the ANN together predict the 24-hour electricity demand. The results show that utilizing the LM and BR algorithms delivers a highly efficient architecture for renewable power estimation demand.

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PI-controlled ANN-based Energy Consumption Forecasting for Smart Grids

Cited by 8 publications

References 0 publications

Intelligent Energy Management with IoT Framework in Smart Cities Using Intelligent Analysis: An Application of Machine Learning Methods for Complex Networks and Systems

Intelligent Energy Management with IoT Framework in Smart Cities Using Intelligent Analysis: An Application of Machine Learning Methods for Complex Networks and Systems

PSO Based Optimized Ensemble Learning and Feature Selection Approach for Efficient Energy Forecast

Short-Term Forecasting of Load and Renewable Energy Using Artifical Neural Network

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