Development of Easily Accessible Electricity Consumption Model Using Open Data and GA-SVR

Wang, Seunghyeon; Hae, Hyeonyong; Kim, Juhyung

doi:10.3390/en11020373

Cited by 9 publications

(8 citation statements)

References 47 publications

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“…This chapter is divided into 2 parts: data description and experimental results. To demonstrate completely the performance of WindNet proposed in this paper, this chapter will also include comparisons of very popular and commonly used machine learning algorithms, such as support vector machine (SVM) [33][34][35][36][37][38], random forest (RF) [39][40][41][42][43][44], decision tree (DT) [45][46][47][48][49][50] and MLP.…”

Section: Resultsmentioning

confidence: 99%

A Short-Term Wind Speed Forecasting Model by Using Artificial Neural Networks with Stochastic Optimization for Renewable Energy Systems

Huang

Kuo

2018

Energies

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To efficiently manage unstable wind power generation, precise short-term wind speed forecasting is critical. To overcome the challenges in wind speed forecasting, this paper proposes a new convolutional neural network algorithm for short-term forecasting. In this paper, the forecasting performance of the proposed algorithm was compared to that of four other artificial intelligence algorithms commonly used in wind speed forecasting. Numerical testing results based on data from a designated wind site in Taiwan were used to demonstrate the efficiency of above-mentioned proposed learning method. Mean absolute error (MAE) and root-mean-square error (RMSE) were adopted as accuracy evaluation indexes in this paper. Experimental results indicate that the MAE and RMSE values of the proposed algorithm are 0.800227 and 0.999978, respectively, demonstrating very high forecasting accuracy.

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Section: Resultsmentioning

confidence: 99%

A Short-Term Wind Speed Forecasting Model by Using Artificial Neural Networks with Stochastic Optimization for Renewable Energy Systems

Huang

Kuo

2018

Energies

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“…In order to fully demonstrate the performance of the EPNet proposed in this paper, this chapter includes comparisons between Support Vector Machine (SVM) [25][26][27][28][29][30], Random Forest (RF) [31][32][33][34][35][36], Decision Tree (DT) [37][38][39][40][41][42], MLP, CNN and LSTM. Figure 6 is the Electric Power Markets (PJM) Regulation Zone Preliminary Billing Data [43] used in this experiment, this data records the regulation market capacity clearing price of every half hour in 2017.…”

Section: Resultsmentioning

confidence: 99%

An Electricity Price Forecasting Model by Hybrid Structured Deep Neural Networks

2018

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Electricity price is a key influencer in the electricity market. Electricity market trades by each participant are based on electricity price. The electricity price adjusted with the change in supply and demand relationship can reflect the real value of electricity in the transaction process. However, for the power generating party, bidding strategy determines the level of profit, and the accurate prediction of electricity price could make it possible to determine a more accurate bidding price. This cannot only reduce transaction risk, but also seize opportunities in the electricity market. In order to effectively estimate electricity price, this paper proposes an electricity price forecasting system based on the combination of 2 deep neural networks, the Convolutional Neural Network (CNN) and the Long Short Term Memory (LSTM). In order to compare the overall performance of each algorithm, the Mean Absolute Error (MAE) and Root-Mean-Square error (RMSE) evaluating measures were applied in the experiments of this paper. Experiment results show that compared with other traditional machine learning methods, the prediction performance of the estimating model proposed in this paper is proven to be the best. By combining the CNN and LSTM models, the feasibility and practicality of electricity price prediction is also confirmed in this paper.

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“…In the European Union, about 40% of the energy is consumed in buildings, of which 63% are residential ones [18]. There is a great potential in improving energy performance by retrofitting existing buildings [19], and it has been proven by research on topics such as the retrofitting method [20][21][22], energy consumption analysis [23,24], life-cycle cost analysis [25,26] and decision-making on retrofitting [27].…”

Section: External Wall Insulationmentioning

confidence: 99%

Image Processing for Sustainable Remodeling: Introduction to Real-time Quality Inspection System of External Wall Insulation Works

et al. 2019

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The external wall insulation method was introduced to enhance the energy efficiency of existing buildings. It does not cause a decrease of inner space and costs less in comparison to methods that insert insulation panels inside walls. However, it has been reported that external wall insulation boards are disconnecting from walls due to malfunctions of the adhesive. This causes not only repair costs, but also serious injury to pedestrians. Separation problems occur when the bonded positions are incorrect and/or the total area and thickness of the adhesive is smaller than the required amount. A challenge is that these faults can hardly be inspected after installing boards. For this reason, a real-time inspection system is necessary to detect potential failure during adhesive works. Position, area and thickness are major aspects to inspect, and thus a method to process image data of these seems efficient. This paper presents a real-time quality inspection system introducing image processing technology to detect potential errors during adhesive works of external wall insulation, and it is predicted to contribute to achieving sustainable remodeling construction by reducing squandered material and labor costs. The system consists of a graphic data creation module to capture the results of adhesive works and a quality inspection module to judge the pass or fail of works according to an algorithm. A prototype is developed and validated against 100 panels with 800 adhesive points.

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Development of Easily Accessible Electricity Consumption Model Using Open Data and GA-SVR

Cited by 9 publications

References 47 publications

A Short-Term Wind Speed Forecasting Model by Using Artificial Neural Networks with Stochastic Optimization for Renewable Energy Systems

A Short-Term Wind Speed Forecasting Model by Using Artificial Neural Networks with Stochastic Optimization for Renewable Energy Systems

An Electricity Price Forecasting Model by Hybrid Structured Deep Neural Networks

Image Processing for Sustainable Remodeling: Introduction to Real-time Quality Inspection System of External Wall Insulation Works

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