**1. Introduction**

Electricity has become a vital part of the life of modern society nowadays. It is said that electricity access is an essential factor to enable the economic growth of a country or region [1]. Many studies also imply that the interruption of electricity supply has a severe impact on business and residential customers [2–4], where total electricity blackout can cost up to billions of dollars of economic activity [5]. These emphasize the importance of reliable and stable electricity supply to our current society.

One of the critical tasks in securing the electricity system's reliability is maintaining the balance between electricity supply and demand. In current large power systems, the task is done by adjusting the power generated from generation units in the systems to a forecasted system electricity demand. Failure to do this correctly may cause the instability of the power system or even a blackout. On the other hand, low accuracy of electricity demand forecasting may also cause inefficient and costly operation of the generation units caused by the requirements of higher capacity of spinning reserve generators and lower efficiency of thermal generators [6]. The latter may also lead to higher carbon emissions which contribute to global temperature rises or global warming [7]. Inevitably, the accuracy of electricity demand forecasting is paramount in electric power system planning and operation.

There are two approaches for estimating energy use: statistical techniques and artificial intelligence [8]. In recent years, artificial intelligence has accelerated, with one of its applications being to improve the control of the current generation system. Predicting electrical

**Citation:** Aisyah, S.; Simaremare, A.A.; Adytia, D.; Aditya, I.A.; Alamsyah, A. Exploratory Weather Data Analysis for Electricity Load Forecasting Using SVM and GRNN, Case Study in Bali, Indonesia. *Energies* **2022**, *15*, 3566. https:// doi.org/10.3390/en15103566

Academic Editors: Luis Hernández-Callejo, Sergio Nesmachnow and Sara Gallardo Saavedra

Received: 30 March 2022 Accepted: 4 May 2022 Published: 12 May 2022

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**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

loads for energy consumption is no longer a novel concept, as it can be accomplished through machine learning to predict future energy consumption points [9]. Numerous studies have been conducted because it is critical to understand the prediction of electrical energy consumption. For example, in 2018, Li and Zhang completed a short-term forecasting of electricity consumption in Shanghai by using grey prediction model [10]. Tian et al. predicted short-term electrical energy consumption using a combination model between STL (Seasonal and Trend decomposition using Loess) and GRU in the same year. They made predictions for the next 3 to 10 days using a combination model between STL (Seasonal and Trend decomposition using Loess) and GRU. When compared to GRU and SVM, GRU produces better results [11]. Hamdoun et al. projected electrical energy by comparing two different approaches, namely statistics and machine learning, to see more accuracy. They found that the prediction model based on machine learning produced the best results and had the lowest error rate among the findings they obtained [12]. Using a combination of the FPA (Flower Pollination Algorithm) model to optimize the Feedforward Neural Network (FNN), Zhao et al. made a short-term prediction of electricity consumption in 2020, then compared it with the SVR and RBFN models. The FPA-FNN model produced good results, with MAPE values of 1.41 percent and RMSE [13]. The Nonlinear Autoregressive (NARM) model was used to predict the electricity load for the next month for the energy management system in 2019. Ahmad and Chen then compared the NARM model to the Random Forest model and the linear model using stepwise regression in the case of ISO New England using the results obtained. They discovered that when compared to the other two models, the NARM model produced the best results [14].

Several studies have shown that weather parameters can affect the electricity load and need to be incorporated in power system planning and demand forecasting [15], both for short-term and long-term system planning [16]. Some studies evaluate the effect of weather parameters on the electricity system at regional and country levels, such as Algeria [17] and Turkey [18]. Other studies evaluated at a lower level, such as building electricity demand or residential house electricity consumption [19,20].

Aisyah and Simaremare investigate the correlation between weather parameters and electricity load in Bali by using three different weather source data, i.e., GFS, ERA5, and observation data from AWS (Automatic Weather Station) BMKG [21]. They conclude that three weather parameters are highly correlated with electricity load in Bali, i.e., temperature, wind speed, and solar radiation. This paper investigates which weather parameters affect electricity consumption in an isolated area by calculating the correlation coefficient with electricity load data. Bali has a significant increase in electricity consumption, and the Island does not have conventional resources [22], so it is crucial to estimate the electricity load for the future. That is by investigating which weather parameters affect most the electricity consumption. Additionally, to our best knowledge, no published research yet on the machine learning area was conducted for the electricity load forecasting in Bali. Thus, we chose Bali Island in Indonesia as a case study. Moreover, we also developed electricity load forecasting using two machine learning models: the Support Vector Regression (SVR) and the Generalized Regression Neural Network (GRNN), with weather parameter data and consumer characteristics as input for the machine learning models.

The SVM is one of the machine learning models that is usually used to solve regression and classification problems. It performs efficiently for time series prediction, especially for seasonal data [23]. Moreover, the SVM also effectively prevents overfitting problems by implementing Structural Risk Minimization (SRM) [24]. GRNN is simple to train and gives a satisfactory prediction, modeling, mapping, and interpolation [25,26]. It also performs efficiently for continuous data [27]. It has a higher learning speed than RBF [28]. To determine which weather parameters have the most significant effects on the electrical load, we create scenarios by gradually increasing the number of weather parameters used as features. Moreover, we also add scenarios in which moving average (MA) of electricity load data is used as a feature for the machine learning models. The innovations in this paper are as follows: firstly, we introduce a technique for feature selection from weather parameters, in which the selected features are used as the inputs to design machine-learning-based electricity forecasting. In [9,29], deep networks are used to make an electricity forecasting model, but they did not make feature selection for weather parameters. Secondly, weather parameters are used as features, but we also consider moving average (MA) data (daily, weekly, and monthly MA) as input for the machine-learning-based electricity load forecasting.

The content of this paper is as follows. Section 2 discusses electricity load data and some weather parameters in Bali and two machine learning models used. We discuss exploratory data analysis between weather parameters and electricity load data in Section 3. It is then followed by descriptions of obtained results and some discussions in Section 4. We conclude the paper in the final section.
