Prediction of Electric Power Production and Consumption for the CETATEA Building Using Neural Networks

Turcu, Flaviu; Lazar, Andrei; Rednic, Vasile; Rosca, Gabriel; Zamfirescu, Ciprian; Puşchiţă, Emanuel

doi:10.3390/s22166259

Cited by 3 publications

(2 citation statements)

References 56 publications

(70 reference statements)

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“…Decision tree (DT) has been used for building energy demand modeling [45]; • The case of applying support vector regression (SVR) in power demand forecasting is in [46]; • The case of applying random forest (RF) for an hourly building energy prediction problem [47]; • An artificial neural network (NN) has been successively applied, including in the energy industry [48].…”

Section: Machine Learning Algorithmsmentioning

confidence: 99%

Weather-Based Prediction of Power Consumption in District Heating Network: Case Study in Finland

Vakhnin,

Ryzhikov,

Brester

et al. 2024

Energies

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Accurate prediction of energy consumption in district heating systems plays an important role in supporting effective and clean energy production and distribution in dense urban areas. Predictive models are needed for flexible and cost-effective operation of energy production and usage, e.g., using peak shaving or load shifting to compensate for heat losses in the pipeline. This helps to avoid exceedance of power plant capacity. The purpose of this study is to automate the process of building machine learning (ML) models to solve a short-term power demand prediction problem. The dataset contains a district heating network’s measured hourly power consumption and ambient temperature for 415 days. In this paper, we propose a hybrid evolutionary-based algorithm, named GA-SHADE, for the simultaneous optimization of ML models and feature selection. The GA-SHADE algorithm is a hybrid algorithm consisting of a Genetic Algorithm (GA) and success-history-based parameter adaptation for differential evolution (SHADE). The results of the numerical experiments show that the proposed GA-SHADE algorithm allows the identification of simplified ML models with good prediction performance in terms of the optimized feature subset and model hyperparameters. The main contributions of the study are (1) using the proposed GA-SHADE, ML models with varying numbers of features and performance are obtained. (2) The proposed GA-SHADE algorithm self-adapts during operation and has only one control parameter. There is no fine-tuning required before execution. (3) Due to the evolutionary nature of the algorithm, it is not sensitive to the number of features and hyperparameters to be optimized in ML models. In conclusion, this study confirms that each optimized ML model uses a unique set and number of features. Out of the six ML models considered, SVR and NN are better candidates and have demonstrated the best performance across several metrics. All numerical experiments were compared against the measurements and proven by the standard statistical tests.

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Section: Machine Learning Algorithmsmentioning

confidence: 99%

Weather-Based Prediction of Power Consumption in District Heating Network: Case Study in Finland

Vakhnin,

Ryzhikov,

Brester

et al. 2024

Energies

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“…NARX is a recurrent dynamic neural network, has feedback connections which enclose several layers of the network for nonlinear time series prediction [56]. NARX is a partial recurrent neural network (RNN) as its memory is embedded into the network [57]. Five inputs for training and simulation are employed: in-plane irradiance, backsheet panel temperature, airmass, clearness index and DC voltage of the inverter.…”

Section: Cascade-forward Autoregression Model (Network4)mentioning

confidence: 99%

Comparative Performance Analysis of a Grid-Connected Photovoltaic Plant in Central Greece after Several Years of Operation Using Neural Networks

Ρουμπακιάς

Stamatelos

2023

Sustainability

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The increasing installed volume of grid-connected PV systems in modern electricity networks induces variability and uncertainty factors which must be addressed from several different viewpoints, including systems’ protection and management. This study aims to estimate the actual performance and degradation of photovoltaic (PV) parks in Central Greece after several years of operation. Monitoring data over several years are analyzed and filtered, the performance ratio and normalized efficiency are computed, and five different ANNs are employed: (i) a feed-forward network (one hidden layer); (ii) a deep feed-forward network (two hidden layers); (iii) a recurrent neural network; (iv) a cascade-forward network; and (v) a nonlinear autoregressive network. The following inputs are employed: in-plane irradiance; backsheet panel temperature; airmass; clearness index; and DC voltage of the inverter. Monitoring data from an 8-year operation of a grid-connected PV system are employed for training, testing, and validation of these networks. They act as a baseline, built from the first year, and the computed metrics act as indicators of faults or degradation. Best accuracy is reached with the DFFNN. The ANNs are trained with data from the first year of operation, and output prediction is carried out for the remaining years. Annual electricity generation exceeds 1600 kWh /kWp, and MAPE values show an increasing trend over the years. This fact indicates a possible change in PV performance.

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Potential impacts of climate change on global solar radiation and PV output using the CMIP6 model in West Africa

Agbor¹,

Udo²,

Ewona³

et al. 2023

Cleaner Engineering and Technology

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Prediction of Electric Power Production and Consumption for the CETATEA Building Using Neural Networks

Cited by 3 publications

References 56 publications

Weather-Based Prediction of Power Consumption in District Heating Network: Case Study in Finland

Weather-Based Prediction of Power Consumption in District Heating Network: Case Study in Finland

Comparative Performance Analysis of a Grid-Connected Photovoltaic Plant in Central Greece after Several Years of Operation Using Neural Networks

Potential impacts of climate change on global solar radiation and PV output using the CMIP6 model in West Africa

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