Model Hibrida ARIMAX dan Deep Learning Neural Network untuk Peramalan Beban Listrik Jangka Pendek di PT. Indonesia Power UP Bali

Energi listrik tidak dapat langsung disimpan dalam skala besar dan hanya dapat digunakan saat dibutuhkan saja. Oleh karena itu energi listrik yang dibangkitkan di pembangkit harus sama dengan energi listrik yang digunakan oleh konsumen. Prediksi listrik yang tepat pada suatu daerah sangat diperlukan untuk mengoptimalkan persediaan kebutuhan listrik. Penelitian ini dilakukan bertujuan untuk menerapkan metode Hibrida ARIMAX dan Deep Learning Neural Network untuk peramalan beban listrik jangka pendek. Data yang digunakan pada penelitian ini adalah data beban listrik mulai Januari 2014 hingga Desember 2017 sebanyak 1461 observasi. Kajian yang digunakan dibagi menjadi dua kajian yaitu kajian simulasi dan kajian terapan. Hasil kajian simulasi menunjukkan bahwa metode Hibrida ARIMAX dan Deep Learning Neural Network menghasilkan hasil peramalan yang lebih baik untuk horizon medium dan long, sementara hasil lebih beragam diperoleh pada horizon short. Untuk kajian terapan, menunjukkan bahwa hasil peramalan menggunakan metode Deep Learning Neural Network menghasilkan hasil ramalan yang lebih baik untuk horizon medium dan long, sementara Hibrida ARIMAX dan Deep Learning Neural Network mendominasi pada horizon short. Pada kedua kajian model Hibrida ARIMAX-DLNN tidak selalu lebih unggul dibanding metode lainnya. Hal ini membuktikan bahwa metode yang lebih kompleks tidak selalu memberikan nilai akurasi ramalan yang lebih baik. Peramalan beban listrik dilakukan berdasarkan metode terbaik yang diperoleh pada horizon short dikarenakan pada umumnya semakin pendek periode peramalan maka akurasi yang dihasilkan semakin baik. =========================================================== Electrical energy can not be directly stored on a large scale and can only be used when needed only. Therefore, the electrical energy generated in the power plant must be equal to the electrical energy used by the consumer. Precise electrical prediction in a region is needed to optimize the supply of electricity needs. The aim of this research is to apply Hybrid ARIMAX and Deep Learning Neural Network method to forecast short-term electrical load. Data used in this research is electric load data from January 2014 to December 2017 as many as 1461 observations. The study used is divided into two studies namely simulation studies and applied studies. The results of the simulation study show that the Hybrid ARIMAX and Deep Learning Neural Network method produce better forecasting results for medium and long horizons, while more diverse results are obtained on the short horizon. For applied studies, indicating that forecasting results using the Deep Learning Neural Network method resulted in better outcomes for medium and long horizons, while Hybrid ARIMAX and Deep Learning Neural Network method dominated on the short horizon. In both studies the Hybrid ARIMAX-DLNN model is not always superior to other methods. This proves that more complex methods do not always provide better prediction accuracy values. Power load forecasting is based on the best method obtained on the short horizon because in general the shorter the forecast period the better the accuracy

Toward efficient energy systems based on natural gas consumption prediction with LSTM Recurrent Neural Networks

Finding suitable forecasting methods for an effective management of energy resources is of paramount importance for improving the efficiency in energy consumption and decreasing its impact on the environment. Natural gas is one of the main sources of electrical energy in Algeria and worldwide. To address this demand, this paper introduces a novel hybrid forecasting approach that resolves the two-stage method's deficiency, by designing a Multi Layered Perceptron (MLP) neural network as a nonlinear forecasting monitor. This model estimates the next day gas consumption profile and selects one of several local models to perform the forecast. The study focuses firstly on an analysis and clustering of natural gas daily consumption profiles, and secondly on building a comprehensive Long Short Term Memory (LSTM) recurrent models according to load behavior. The results are compared with four benchmark approaches: the MLP neural network approach, LSTM, seasonal time series with exogenous variables models and multiple linear regression models. Compared with these alternative approaches and their high dependence on historical loads, the proposed approach presents a new efficient functionality. It estimates the next day consumption profile, which leads to a significant improvement of the forecasting accuracy, especially for days with exceptional customers consumption behavior change

A novel improved model for building energy consumption prediction based on model integration

Building energy consumption prediction plays an irreplaceable role in energy planning, management, and conservation. Constantly improving the performance of prediction models is the key to ensuring the efficient operation of energy systems. Moreover, accuracy is no longer the only factor in revealing model performance, it is more important to evaluate the model from multiple perspectives, considering the characteristics of engineering applications. Based on the idea of model integration, this paper proposes a novel improved integration model (stacking model) that can be used to forecast building energy consumption. The stacking model combines advantages of various base prediction algorithms and forms them into “meta-features” to ensure that the final model can observe datasets from different spatial and structural angles. Two cases are used to demonstrate practical engineering applications of the stacking model. A comparative analysis is performed to evaluate the prediction performance of the stacking model in contrast with existing well-known prediction models including Random Forest, Gradient Boosted Decision Tree, Extreme Gradient Boosting, Support Vector Machine, and K-Nearest Neighbor. The results indicate that the stacking method achieves better performance than other models, regarding accuracy (improvement of 9.5%–31.6% for Case A and 16.2%–49.4% for Case B), generalization (improvement of 6.7%–29.5% for Case A and 7.1%-34.6% for Case B), and robustness (improvement of 1.5%–34.1% for Case A and 1.8%–19.3% for Case B). The proposed model enriches the diversity of algorithm libraries of empirical models

Long-Term Load Forecasting Considering Volatility Using Multiplicative Error Model

Long-term load forecasting plays a vital role for utilities and planners in terms of grid development and expansion planning. An overestimate of long-term electricity load will result in substantial wasted investment in the construction of excess power facilities, while an underestimate of future load will result in insufficient generation and unmet demand. This paper presents first-of-its-kind approach to use multiplicative error model (MEM) in forecasting load for long-term horizon. MEM originates from the structure of autoregressive conditional heteroscedasticity (ARCH) model where conditional variance is dynamically parameterized and it multiplicatively interacts with an innovation term of time-series. Historical load data, accessed from a U.S. regional transmission operator, and recession data for years 1993-2016 is used in this study. The superiority of considering volatility is proven by out-of-sample forecast results as well as directional accuracy during the great economic recession of 2008. To incorporate future volatility, backtesting of MEM model is performed. Two performance indicators used to assess the proposed model are mean absolute percentage error (for both in-sample model fit and out-of-sample forecasts) and directional accuracy.Comment: 19 pages, 11 figures, 3 table

A novel ensemble method for electric vehicle power consumption forecasting: Application to the Spanish system

The use of electric vehicle across the world has become one of the most challenging issues for environmental policies. The galloping climate change and the expected running out of fossil fuels turns the use of such non-polluting cars into a priority for most developed countries. However, such a use has led to major concerns to power companies, since they must adapt their generation to a new scenario, in which electric vehicles will dramatically modify the curve of generation. In this paper, a novel approach based on ensemble learning is proposed. In particular, ARIMA, GARCH and PSF algorithms' performances are used to forecast the electric vehicle power consumption in Spain. It is worth noting that the studied time series of consumption is non-stationary and adds difficulties to the forecasting process. Thus, an ensemble is proposed by dynamically weighting all algorithms over time. The proposal presented has been implemented for a real case, in particular, at the Spanish Control Centre for the Electric Vehicle. The performance of the approach is assessed by means of WAPE, showing robust and promising results for this research field.Ministerio de Economía y Competitividad Proyectos ENE2016-77650-R, PCIN-2015-04 y TIN2017-88209-C2-R