Long-Term Forecasting of Electrical Loads in Kuwait Using Prophet and Holt–Winters Models

Abstract

The rapidly increasing population growth and expansion of urban development are undoubtedly two of the main reasons for increasing global energy consumption. Accurate long-term forecasting of peak load is essential for saving time and money for countries’ power generation utilities. This paper introduces the first investigation into the performance of the Prophet model in the long-term peak load forecasting of Kuwait. The Prophet model is compared with the well-established Holt–Winters model to assess its feasibility and accuracy in forecasting long-term peak loads. Real data of electric load peaks from Kuwait powerplants from 2010 to 2020 were used for the electric load peaks, forecasting the peak load between 2020 and 2030. The Prophet model has shown more accurate predictions than the Holt–Winters model in five statistical performance metrics. Besides, the robustness of the two models was investigated by adding Gaussian white noise of different intensities. The Prophet model has proven to be more robust to noise than the Holt–Winters model. Furthermore, the generalizability test of the two models has shown that the Prophet model outperforms the Holt–Winters model. The reported results suggest that the forecasted maximum peak load is expected to reach 18,550 and 19,588 MW for the Prophet and Holt–Winters models by 2030 in Kuwait. The study suggests that the best months for scheduling the preventive maintenance for the year 2020 and 2021 are from November 2020 until March 2021 for both models.

Electricity Consumption Forecasting Based on a Bidirectional Long-Short-Term Memory Artificial Neural Network

“…In order to attain these goals, scientists have used different methods and carried out numerous performance improvement techniques, such as: SVR [3,11,28], LSTM ANNs [3,4,10,11,26,28], RNN [5,28], BiLSTM ANN [5], Copula-DBN [7], DRNN-LSTM [8], CNN-RNN [9], the Prophet and Holt-Winters long-term forecasting models [13], CNNs [4,6,14,25], MLR [24,25], SVM [24], and ANFIS [24]. In contrast with these, within our article, we devised and developed a forecasting method for the hourly month-ahead electricity consumption based on a BiLSTM ANN enhanced with a multiple simultaneously decreasing delays approach coupled with FITNETs.…”

“…Analyzing the scientific literature, one can remark that when forecasting the electricity consumption, different forecasting time horizons are of interest, encompassing short- [3,14,19,22,24,26,28], medium- [19,21,26], and long-term timeframes [10,13,14,20,[23][24][25]64], each of them bringing their own particular advantages in line with the actual requirements and business needs of the contractors. We targeted the hourly month-ahead electricity prediction, considering the numerous benefits that such a forecast brings to the largeelectricity commercial center-type consumer, ranging from the negotiation and choosing of the most appropriate hourly billing tariffs and correct estimations for the month-ahead electricity consumption submitted to the dispatcher to proper decisional support in what concerns the return on investment in more energy efficient equipment and assessing expanding options.…”

“…In the scientific article [13], taking into account that attaining a long-term forecast of the electricity consumption is of paramount importance from both the monetary and time resources perspectives, Almazrouee et al perform a comparative analysis between the Prophet and Holt-Winters long-term forecasting models in terms of prediction accuracy and efficiency. When conducting their study, the authors used real electricity consumption datasets ranging between the years 2010 and 2020 from the powerplants located in Kuwait in order to predict the electricity consumption for the following 10 years, up to the year 2030.…”

Estimation of Gourami Supplies Using Gradient Boosting Decision Tree Method of XGBoost

“…MAPE can be found using the formula in Equation 1. 1The explanation of Equation 1is as follows: = actual value on data t = predicted value on data t = the amount of data A good regression model is a model that has a MAPE smaller than 10% [17]. MAPE according to [18] is divided into 4 of bad, reasonable, good, and excellent.…”

Electricity requirement prediction using time series and Facebooks PROPHET

“…Five metrics were used for comparing the performances of the said methods. From the experimental results, it was found that the PROPHET model achieves more accurate prediction when compared to the Holt-winters concerning the generalization test (18) .…”