Modeling of Daily Solar Energy on a Horizontal Surface for Five Main Sites in Malaysia
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This paper presents models for global and diffuse solar energy on a horizontal surface for main five sites in Malaysia. The global solar energy is modeled using linear, nonlinear, fuzzy logic, and artificial neural network (ANN) models, while the diffuse solar energy is modeled using linear, nonlinear, and ANN models. Three statistical values are used to evaluate the developed solar energy models, namely, the mean absolute percentage error, MAPE; root mean square error, RMSE; and mean bias error, MBE. The results showed that the ANN models are superior compared with the other models in which the MAPE in calculating the global solar energy in Malaysia by the ANN model is 5.38%, while the MAPE for the linear, nonlinear, and fuzzy logic models are 8.13%, 6.93%, and 6.71%, respectively. The results for the diffuse solar energy showed that the MAPE of the ANN model is 1.53%, while the MAPE of the linear and nonlinear models are 4.35% and 3.74%, respectively. The accurate ANN models can therefore be used to predict solar energy in Malaysia and nearby regions.Consumption
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Pork is one of the main methods for human intake of animal protein, and its price level will directly affect people’s daily lives. In order to realize the prediction of the prices in the live pig (mid-term) market, based on monthly data provided by China National Database, in this paper we propose a combination of artificial neural network models based on bidirectional recurrent neural network and bidirectional long short-term memory as the backbone network. The prediction errors achieved on our data set for Mean Square Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Symmetric Mean Absolute Percentage Error (SMAPE) are 0.48, 0.69, 0.53, 3.37%, 3.37% respectively. Compared with other deep learning models, the error of this method is small, which shows that it has the ability to predict the time series of pork price.
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The spread of COVID-19, namely SARS-CoV-2, has created a disastrous situation around the world causing an unclear future. Machine Learning (ML) and Deep Learning (DL) have a vital role in tracking the disease, predicting the outgrowth of the epidemic, and outlining strategies and policies to control its spread. Despite the inaccuracies of medical forecasts, the numbers of COVID-19 cases forecasts provide us with valuable information for recognizing the present and preparing for the future. This study proposes a time series based deep learning model, specifically the Long Short-Term Memory (LSTM) model. The model will predict the active, confirmed, deaths and recovered cases for 7 days ahead for Egypt and Saudi Arabia based on real-time data. The Egypt prediction model achieves Mean Absolute Percentage Error (MAPE) of 3.26150, a Root Mean Square Error (RMSE) of 0.0144, a Mean Square Error (MSE) of 0.0002, and a Mean Absolute Error (MAE) of 0.0092. While the Saudi prediction model obtains a MAPE of 5.0553, a RMSE of 0.0170, a MSE of 0.0002, and a MAE of 0.0150.
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An efficient management and better scheduling by the power companies are of great significance for accurate electrical load forecasting. There exists a high level of uncertainties in the load time series, which is challenging to make the accurate short-term load forecast (STLF), medium-term load forecast (MTLF), and long-term load forecast (LTLF). To extract the local trends and to capture the same patterns of short, and medium forecasting time series, we proposed long short-term memory (LSTM), Multilayer perceptron, and convolutional neural network (CNN) to learn the relationship in the time series. These models are proposed to improve the forecasting accuracy. The models were tested based on the real-world case by conducting detailed experiments to validate their stability and practicality. The performance was measured in terms of squared error, Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Mean Absolute Error (MAE). To predict the next 24 hours ahead load forecasting, the lowest prediction error was obtained using LSTM with R2 (0.5160), MLP with MAPE (4.97), MAE (104.33) and RMSE (133.92). To predict the next 72 hours ahead of load forecasting, the lowest prediction error was obtained using LSTM with R2 (0.7153), MPL with MAPE (7.04), MAE (125.92), RMSE (188.33). Likewise, to predict the next one week ahead load forecasting, the lowest error was obtained using CNN with R2 (0.7616), MLP with MAPE (6.162), MAE (103.156), RMSE (150.81). Moreover, to predict the next one-month load forecasting, the lowest prediction error was obtained using CNN with R2 (0.820), MLP with MAPE (5.18), LSTM with MAE (75.12) and RMSE (109.197). The results reveal that proposed methods achieved better and stable performance for predicting the short, and medium-term load forecasting. The findings of the STLF indicate that the proposed model can be better implemented for local system planning and dispatch, while it will be more efficient for MTLF in better scheduling and maintenance operations.
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In conducting load forecasting, the accuracy of forecasting is an important aspect in planning and managing electricity. Thus, a new hybrid model is presented in this paper, which combines the Group Method of Data Handling, Least Square Support Vector Machine and Artificial Bee Colony (GLSSVM- ABC) for building load forecasting. Its performance accuracy has been compared with other methods by using the Mean Absolute Percentage Error (MAPE) and Root Means Square Error (RMSE). It was found that the proposed method has resulted in better performance accuracy in terms of both MAPE and RMSE. The MAPE analysis showed an increase in performance accuracy of more than 7 percent when compared to other methods. The RMSE analysis showed an increase in performance accuracy of more than 5 percent when compared to other methods. The results in this study showed that the proposed method is proven to be effective and has great potential for accurate building load forecasting.
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본 연구에서는 Long Short Term Memory (LSTM) 신경망과 Gated Recurrent Unit(GRU) 신경망을 Internet of Things (IoT) 파워미터에 적용하여 단기 전력사용량 예측방법을 제안하고, 실제 가정의 전력사용량 데이터 를 토대로 예측 성능을 분석한다. 성능평가 지표로써 Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Mean Percentage Error (MPE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE) 를 이용한다. 실험 결과는 GRU 기반의 모델이 LSTM 기반의 모델에 비해 MAPE 기준으로 4.52%, MPE 기준으로 5.59%만큼의 성능개선을 보였다.
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The geographical position of the Kingdom of Saudi Arabia has significant potentials for utilizing renewable energy resources, which aligns with the country's vision for 2030. This paper proposes a solution to achieve energy sustainability by forecasting future load demands through adopting three different scenarios. We used the outsourced Individual Household Electric Power Consumption Dataset, University of California-Irvine repository, for testing our proposed system. We utilized the Long Short-term Memory-Recurrent Neural Network (LSTM-RNN) algorithm to estimate the whole house power consumption for different horizons: every 15 minutes, daily, weekly, and monthly. Next, we evaluated the performance of the system by Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Square Error (RMSE), and $R^{2}$ score metrics. Then, we applied the Mean Absolute Percentage Error (MAPE) to find its accuracy. The results showed that the monthly forecasting interpretation scenario was the best performing model. That scenario used (n-1) months for training and the last month for testing. The scores for that model were 0.034 (MAE), 0.001 (MSE), 0.034 (RMSE), and 97.16% (accuracy). The constructed model successfully achieved its goals of predicting the active power of the household and now can be accommodated on energy applications not only in Saudi Arabia but also in any other country.
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최근 일별 최대 전력수요 예측은 전력설비 계획 및 운용에 매우 중요한 사안으로 주목받고 있다. 본 연구는 일별 최대 전력수요 예측을 위하여 대표적 시계열 모형을 소개하고, 예측의 성능 비교를 위하여 RMSE(Root mean squared error)와 MAPE(Mean absolute percentage error)를 사용한다. 연구결과로 보완된 Holt-Winters 모형과 Reg-ARIMA 모형이 다른 모형에 비하여 우수한 예측 성능을 보였다. Forecasting the daily peak load for electricity demand is an important issue for future power plants and power management. We first introduce several time series models to predict the peak load for electricity demand and then compare the performance of models under the RMSE(root mean squared error) and MAPE(mean absolute percentage error) criteria.
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