{'fraction_non_alphanumeric': 0.029387244685285537, 'fraction_numerical': 0.013373627900514103, 'mean_word_length': 4.942002063983488, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Bootstrap aggregating approach to short-term load forecasting using meteorological parameters for demand side management in the North-Eastern Region of India

Electricity is an essential commodity that must be generated in response to demand. Hydroelectric power plants, fossil fuels, nuclear energy, and wind energy are just a few examples of energy sources that significantly impact production costs. Accurate load forecasting for a specific region would allow for more efficient management, planning, and scheduling of low-cost generation units and ensuring on-time energy delivery for full monetary benefit. Machine learning methods are becoming more effective on power grids as data availability increases. Ensemble learning models are hybrid algorithms that combine various machine learning methods and intelligently incorporate them into a single predictive model to reduce uncertainty and bias. In this study, several ensemble methods were implemented and tested for short-term electric load forecasting. The suggested method is trained using the influential meteorological variables obtained through correlation analysis and the past load. We used real-time load data from Nagaland’s load dispatch centre in India and meteorological parameters of the Nagaland region for data analysis. The synthetic minority over-sampling technique for regression (SMOTE-R) is also employed to avoid data imbalance issues. The experimental results show that the Bagging methods outperform other models with respect to mean squared error and mean absolute percentage error.


Introduction
Load forecasting is a pivotal step for electric grids' efficient operation and management in the day-ahead electricity market (Amato et al. 2021). Owing to the rapid growth of renewable energy and the implementation of electric vehicles and other emerging technologies, the electric power grid has undergone significant changes in the last few years, both on the supply and demand lines (Andriopoulos et al. 2021). To sustain an equilibrium between supply and demand in this setting, electricity suppliers must take advantage of the next-generation power grid's opportunities.
Furthermore, in the event of unusually high peak energy demand, electric utility companies must be prepared in advance with contingency plans. As a result, it is essential to forecast future load demand to reduce the cost of generating electricity (Li and Jin 2019). Electricity load forecasting is the process of predicting future load based on various features, including weather conditions, time details such as the month and hour, economic conditions, energy tariffs, regional conditions are examples. Since accurate electricity load forecasting is critical in the power system, even minor improvements in load forecasting accuracy can result in substantial cost savings and environmental benefits . Besides that, any forecast error results in a substantial cost increase. As a result, effective load forecasting is critical for power-grid construction, investment, and transactions in order to ensure reliable and cost-effective power system operation (Moradzadeh et al. 2021).
In recent times, researchers have suggested multiple models for forecasting electric load for different periods. Short-term load forecasting(SLF) models forecast electricity demand every half-hour or hour for the next few hours to two weeks. Medium-and long-term models are established when long-term load forecasting is needed (Fan and Hyndman 2011). Regression models are good at estimating stationary time series (Miraftabzadeh et al. 2019). Since the time series of load demand is nonlinear and non-stationary, the Autoregressive Moving Average (ARMA) method (Li and Zhen-gang 2009;Din et al. 2018) and Support Vector Regression (SVR) (Velasco et al. 2018;Yang et al. 2019) methods were proposed. A single model is incapable of accurately capturing the inherent characteristics of electricity load demand. Hence, Li and Zhen-gang (2009) integrated the ARMA approach with the other machine learning methods to boost the efficiency of combined load forecasting. Nepal et al. (2020) developed a hybrid model that combines clustering with the integrated ARMA model.
The SLF problem has been also studied using a variety of sophisticated data-driven models. Fuzzy logic (Sadaei et al. 2014), artificial neural networks (ANNs) (Cecati et al. 2015;Yi et al. 2021), Extreme Learning (Li et al. 2016;Park et al. 2020), and exponential smoothing (Sudheer and Suseelatha 2015) are some of the models that have been used. Numerous hybrid models have been developed by combining multiple models in order to increase forecasting accuracy. Yang et al. suggested an advanced empirical mode decomposition approach to solve the net outcome and the envelope fitting limitations inherent in traditional empirical mode decomposition-based models (Yang et al. 2015). Liang et al. proposed a framework that combines EMD, minimal consistency, maximal relevance, and a neural network with an optimization method (Yi et al. 2019). Talaat et al. suggested a novel approach of a multi-layer frontpropagated neural net and the grasshopper optimization technique to achieve accurate load forecasting (Talaat et al. 2020 The current research makes four significant contributions: (1) For specific-region SLF implementation, the proposed framework makes use of real-time load and meteorological data.
(2) The implementation of the synthetic minority oversampling technique for regression (SMOTE-R) addresses data imbalance.
(3) The most advanced load forecasting models are checked and compared. (4) The proposed method achieves the least mean absolute percentage error by using Bootstrap aggregating (Bagging) models. The remainder of the article is organized as follows: Section 2 deals with the influence of climatic variables on load consumption of Nagaland State. Section 3 addresses the theoretical basis of ensemble learning models. Section 4 discusses the proposed methodology. Section 5 gives the conclusion and future directions.

Geography of Nagaland
Nagaland is located in India's north-eastern section; the north-eastern part of India is situated between 21.57 and 29.30 • North latitude and 88 to 97.30 • East longitude and covers an area of approximately 262,185 km (Borah et al. 2015;Abida Choudhury et al. 2019;Pal et al. 2020). Because the region has a mix of flat and hilly terrain, the climate varies significantly from one location to the next, even if they are not far apart. Myanmar shares borders with Nagaland to the east, Assam to the west, Arunachal Pradesh and Assam to the north, and Manipur to the south. Moreover, peak power shortages of 18.2% and 17.24% during 2013-2014 and 2014-2015, respectively, and energy shortages of 13.7% and 5.04% (Aier et al. 2021). According to 2011 census data, Nagaland has approximately four lakh households, about 2.85 lakh were in rural areas, and 1.15 lakh were in urban areas. Rural regions were electrified in 75.09% of cases, whereas urban areas were in 97.40%. The Nagaland Department of Power is in charge of power generation, transmission, and distribution. The state has enormous hydroelectric potential but lacks power due to the construction of the power plant. It is determined mainly by the distribution of power from central public sector organizations. This reliance on power from other sources necessitates additional financing and inadequacy in power delivery during peak seasons. The load consumption during the year 2018 is illustrated in Fig. 1.
The load profile shows that the load consumption slowly rises from day one to the middle of the year and then gradually drops at the end of the year. It is because Dimapur's winter months are at the beginning and end of the year, and it has a scorching summer because it is a warm and humid place. Winter energy consumption is low because the chilly weather in Dimapur is tolerable. Nonetheless, the summer months are energy demanding due to Dimapur's warm and humid atmosphere, resulting in a rise in energy usage during the summer months. In Nagaland, the rainy season lasts around nine months and has approximately 85-95% relative humidity (Singh et al. 1998). The yearly average temperature in Nagaland is forecast to climb by roughly 1.6 to 1.8 • C. Annual rainfall is also expected to rise between 2020 and 2050 (Mishra et al. 2019).

Humidity
Humidity is considered since a high temperature does not always indicate a hot day. Humans experience heat differently depending not only on the day's temperature but also on the humidity, as human skin utilizes the surrounding air to wick away moisture (in the form of sweat). A high humidity level indicates that the air is completely saturated with water vapor. As a result, sweat evaporates more slowly, making a person feel hotter than the actual temperature number. On the other hand, a low humidity level indicates that the air is dry, which aids in the evaporation of sweat, making a person feel cooler than the actual temperature number. Thus, increased humidity means that more cooling loads (cooling equipment) will be utilized. The same is valid to a certain extent when the humidity level is low enough to support heating loads.

Dew point
The dew point is defined as the temperature at which the partial vapor pressure of water in wet air is sufficient to saturate the air thoroughly (Wood 1970). In other words, the partial vapor pressure at any temperature equals the partial saturation vapor pressure at the dew point. In practice, relative humidity can be perplexing at times. For example, if the outside temperature is 30 • C and the dew point temperature is also 30 • C, the relative humidity will be 100%. If the temperature is 80 • C and the dew point is 60 • C, a relative humidity of 50% is obtained, a person will feel more uncomfortable in the second scenario. Thus, we may see the significance of the dew point. The higher the dew point temperature, the higher the electricity consumption due to increased cooling loads.

Day and time of the year
Electric energy usage varies according to the time of day or range of times of the day. Assume increased electrical energy consumption during the day because most workplaces and schools operate during this period and may require more electricity. Additionally, it is necessary to consider the year's day, as the seasons fluctuate. Thus the conditions for power consumption alter according to the day or range of days of the year. It is also worth noting that weekdays may have different load requirements than weekends. Special events that occur on the same day each year will also increase load demand compared to regular days.

Cloud coverage
With a clear sky, there is an increase in the consumption of cooling loads due to the higher solar radiation (Yuk 1998;Jhajharia et al. 2009). Cloud cover's effect on electricity demand is time-dependent and is dependent on the cloud's height and thickness. For instance, on a hot summer day, if the cloud cover is dense, the heat from the sun is absorbed by the cloud. This has the effect of reducing cooling loads to a certain extent. Whereas, at night, the cooling impact of a cloud is minor due to the absence of sunshine.

Precipitation
Precipitation is a broad phrase that refers to the amount of rainfall, snowfall, hail, and other types of liquid water that fall on a particular location in millimetres (Trenberth 2011;Ray et al. 2021). When precipitation occurs in the form of heavy rain or snow during the winter, the demand for electric energy may increase due to drying loads or, if there is snow, heating loads. On the other side, if it rains or snows during the summer, the demand for electrical energy may decrease.

Wind chill factor
The wind chill factor is the difference in air temperature felt on the skin due to the wind. When the wind blows against a hot body, it replaces the hot air with colder air. If the wind is blowing quickly, the rate of hot air replacement by colder air will be faster. This is referred to as wind chill, and it results in a drop in the temperature of a hot body. This impact results in increased heating load usage throughout the winter season. The wind chill is given by an equation as follows: where WC is wind chill in • C, W S is the wind speed in m/s, T A is the air temperature in • C (Oliveira et al. 2011).

Ensemble learning
Ensemble machine learning is a series of models that train a diverse group of potential learners and incorporate their hypotheses to teach the target's particular viewpoint (Wan and Yang 2013). Ensemble machine learning regressors were evaluated in this analysis, including Ada-Boost, Cat-Boost, Extreme-Gradient-Boost, Light-Gradient-Boost, Random Forest, and Extra-Trees (Pedregosa et al. 2011).

Boosting techniques:
Boosting is an ensemble technique in which every successive model tries to eliminate the previous model subsets' errors, as shown in Fig. 2. Boosting templates such as Ada-Boost, Cat-Boost, Extreme-Gradient-Boost, and Light-Gradient-Boost were introduced. Ada-Boost (Zhao et al. 2019) is used in aggregation with other algorithms to reduce the prediction error. Training a sequence of weak regressor models from the observed data package and combining them into a robust regressor is one way to build the most potent forecast model. Extreme-Gradient-Boost (Georganos et al. 2018) expands Classification And Regression Tree (CART)-compliant classical boosting strategies. The fundamental role in the ensemble tree-boosting method predicts the group's new membership for each iteration. This is achieved in the most efficient way possible, ensuring that forecasts are made with soft regressors continuously constructed over the preceding regressor's fault. Following that, incorrectly classified models are assigned advanced weights, letting the regressor focus on subsequent recurrences' performance. The final categorization is critical because it requires the combined growth of all past decision trees. The evaluation of such regressors depends on the specific function, which defines the training failure and regularization.
Cat-Boost (Jhaveri et al. 2019;Kumar et al. 2019) is an application for gradient boosting that excels at handling categorical results. It makes no attempt to substitute discrete categorical attributes. It determines the average mark value by permuting a random dataset, such as adding the change's identical class value before the specified one. When confronted with definite category features, Cat-Boost avoids over-fitting. Light-Gradient-Boost (Li et al. 2018) is a decision tree-focused ensemble model. The advancing propagation process is used for this system. In each epoch, the residual is close-fitting with a negative slope for studying the decision tree. Light-gradient-Boost (introduced by Microsoft in 2016) is a significant Extreme-Gradient-Boost update that can fix issues by improving the sample size while the node is isolated. Light-Gradient-Boost employs a leaf-based method to increase forecast accuracy (Ke et al. 2017).

Bagging techniques:
The objective of bagging is to achieve a standardized outcome by combining the outcomes of several decision trees. As seen in Fig. 3, the bootstrap aggregating approach employs subsets to maintain a correct representation of the whole set. It is a technique of selecting several random samples from a training feature dataset. Random Forest ) is a learning method that provides planning to construct multiple decision trees, resulting in a class that each tree averages or votes on. By adding a randomness layer to the bagging operation, Genuer et al. suggested a Random-Forest method (Genuer et al. 2010). Random-Forest is helpful for regression, grouping, and feature collection, among other things. Random-Forest has three distinct advantages. To begin, it reduces noise by selecting variables and data at random to create multiple regression trees. Second, Random-Forest can handle highdimensional data and adapts to various datasets. It will work for both continuous and discrete data without the need for normalization. Third, the parallel arithmetic's rapid learning speed reduces computational costs.
Finally, an Extra-Trees regressor uses a meta-learning algorithm to train several randomized decision trees. The averaging method is used to increase prediction accuracy and reduce over-fitting. Each decision tree is evaluated at each intersection by executing the following steps: (1) Select K attributes randomly, (2) calculate the split for each feature, and (3) select the feature that boosts the score using a normalized method of feature selection.

Proposed method
This section addresses the proposed load forecasting model, which uses real-time load data and meteorological parameters as training data. The proposed scheme made use of data analysis collected from the Nagaland Load Dispatch Centre. The proposed system's block diagram is depicted in Fig. 4.
As shown in the block diagram, the proposed system can be explained in three steps: (1) feature engineering, (2) features extraction, (3) use ensemble learning for load forecasting.

Feature engineering
Feature engineering is a method of converting operational variables into functions that best reflect the primary problem in analytical models and improve system performance for data that has not been used before. We used SMOTE-R to solve the data imbalance problem, which is discussed in the next section.

SMOTE for regression (SMOTE-R)
In classification problems, SMOTE is a tool for sampling imbalanced class distributions. This approach has the advantage of combining normal class sub-sampling with minority class over-sampling. Bowyer et al. demonstrate the benefits of this method over other alternate sampling models on several real-world difficulties using several classification  (Chawla et al. 2002). SMOTE-R, a version of SMOTE for regression tasks that correctly estimate rare extreme values, is proposed by Luis Torgo et al., who make a significant contribution (Torgo et al. 2013). The original SMOTE algorithm constructs fake cases with a rare target value using an over-sampling technique. To build these artificial cases, Fernandez et al. implemented an interpolation strategy (Fernández et al. 2018). The procedure involves selecting one of the k close neighbours for each event from a list of observations of uncommon values. These two results result in a new scenario that combines attribute values with the two original examples' values. Both cases and synthetic cases belong to the same class as SMOTE is used to address classification issues using an unique class of concern in terms of the target variable. To build a general structure for predicting rare maximum values in a continuously varying target, the authors used a SMOTE-R technique combined with any other regression algorithm.

Important feature extraction
The feature pre-processing technique removes unimportant features from the CSV data before feeding it to the ensemble model. Feature importance metrics can aid in data interpretation, but they are often used to rank and pick features most relevant to a predictive model. The relative scores will indicate which features are most important to the target and, conversely, which features are least relevant. Using significance scores, feature importance can be used to construct a statistical model. Features with low scores will be removed, while features with high scores will be retained. The feature selection can help to simplify the problem being modelled, speed up the modelling process (deleting features is referred to as dimensionality reduction), and, in some cases, increase the model's efficiency.
To effectively deal with the system's unforeseen operating circumstances, the qualified model must be updated in actual implementations. As a result, a model upgrade mechanism is introduced to boost the generalization ability of the stability assessment model and achieve seamless online evaluation. Variable reduction is a technique for reducing the number of input variables. The more input features there are, the more complex the predictive modelling task becomes. Though dimensional reduction techniques are widely used for data visualization, they may also be used for classifiers or regression models with higher-dimensional data.

Ensemble load forecasting
As discussed in Section 3, we use different ensemble learning models to forecast the load and we found that the Random Forest model is giving the best precision over the remaining ensemble models. The proposed ensemble load forecasting is illustrated using flow chart as seen in Fig. 5.

Blended model
The term blending was used to refer to models that were constructed by stacking hundreds of forecasting models. Blending ensembles are a subclass of stacking in which the meta-model is fitted with predictions from a holdout validation dataset rather than out-of-fold forecasts. A stacking model's architecture consists of two or more base models, which are frequently referred to as zero level models, and a meta-model that aggregates the predictions of the base models, which is referred to as a first level model. The meta-model is trained using out-of-sample predictions generated by basic models.

The dataset and feature extraction
Ensemble SLF model is validated using real-time data from the Nagaland Load Dispatch Center in India's North-  Table 1 provides additional information about the meteorological parameters used in the dataset. The proposed load forecasting model was trained using data from Nagaland's electric load demand between January 1st and December 31st, 2018. Hourly load data and 16 meteorological parameters were obtained over 2 years. Thus a total of 17,520 samples (730 days×24 h=17,520 samples) are being used as a database, as seen in Fig. 6. As a result, the size of the data is 17,520 × 17. This research used feature selection to increase the model's accuracy and effectiveness. Without feature selection, researchers would be forced to conduct additional unnecessary data training with and without specific features in order to assess whether or not those attributes have an impact on the prediction's accuracy.
The current work divides the total data set into training and testing data sets with 80% and 20% split, respectively as seen in Fig. 7. Figure 8 illustrates the histogram plot of the dataset used in training.
We selected features using the feature selection approach based on correlation coefficient and eliminated redundant features by evaluating the feature correlation matrix. The correlation matrix is an important data analysis metric used to analyze data to understand the relationship between different variables and make sound decisions. Each row and column in this matrix represents a variable with a value representing the coefficient of correlation between the variables defined by that row and column.
The Pearson correlation coefficient is the most basic method for determining the association between features and the target variable. The results' interval is [−1, 1], where −1 represents a complete weak association (one variable rises while the other decreases), +1 represents a complete strong association, and 0 represents no linear association. Figure 9 shows the correlation map for the proposed system's training data.
Each cell in the grid represents the correlation coefficient between two variables. It is a square matrix with each row containing a vector and all columns corresponding to the rows. The total number of columns is the same as the total number of rows. A high positive value (near 1.0) implies Along with correlation matrix, we have also used another method for selecting important features, i.e., Recursive Feature Elimination (RFE). The RFE looks for a subset of the test-set attributes that begin with all of them and then successfully eliminates them until the correct list remains in place. This is accomplished by re-fitting the learning model, ranking features by score, discarding the least important features, and re-fitting it. This procedure is replicated until only a few features remain. By using RFE process, obtained the plot between number of features selected and its cross validation score as shown in Fig. 10. From this analysis, three features, i.e., day, time, and dew point, are extracted as important features. The time series feature importance also calculated as seen in Fig. 11.

Tuning of hyperparameters
Hyperparameter tuning is needed to obtain the ideal ensemble weights, which are then necessary to increase the accuracy of the ensemble load forecasting model. Table 2 lists the optimal tuning values for various hyperparameters for various models.

Evaluation of ensemble learning models
In the present study, we have used six ensemble regressor models including Ada-Boost, Extreme-Gradient-Boost, Cat-Boost, Light-Gradient-Boost, Random Forest, and Extra-Trees Regressor models. Long Short-Term Memory (LSTM) and ARMA networks were also implemented and compared to demonstrate the proposed system's feasibility. First, we used cross-validation to determine which ensemble learning model will be the best fit for the proposed SLF. Figure 12 shows the comparison of different machine learning models cross-validation performance with respect to Negative mean square error. The mean absolute error (MAE), mean absolute percentage error (MAPE), and mean square error (MSE) metrics have been used to assess the performance of ensemble learning algorithms. Ifŷ i indicates the expected value of ith sample and y i indicates the corresponding actual value. Then the MSE over n samples may be given as follows: ( 2 ) Fig. 11 Time series feature importance from date-time index   The MSE calculates accuracy by comparing the amount of squared errors between forecasting and real values.The MSE value will evaluate the error variation; the smaller the MSE value, the more stable the prediction model will be. The MAE is a measure of the average amount of combined absolute error between predictions and actual values, which can help avoid error mutual cancellation. As a consequence, MAE will correctly represent the absolute prediction error.
The relative difference between exact errors and real values is denoted by MAPE. The MAPE is a common metric for evaluating regression tasks.

MAPE
The Table 3 summarizes machine learning models' evaluation using the MSE, MAE, and MAPE metrics without feature importance. As seen in the table, Random forest outperformed all other models, including LSTM, ARMA, and machine learning models. All state-of-the-art models were re-evaluated merely based on the important attributes obtained in the previous Section 5.1. The Random forest algorithm, as seen in Table 4, has the lowest MSE (25.463), MAE (3.462), and MAPE (8.482) values of the other algorithms.

Conclusion
In this analysis, an ensemble regressor was used to demonstrate a process of short term load forecasting in North-Eastern part of India. The suggested scheme is dependent on meteorological parameters and historical patterns of load use. At the pre-processing data stage, SMOTE-R analysis was used to solve the data imbalance issue. Numerous ensemble algorithms based on boosting and bagging techniques were investigated, including Ada-Boost, Extreme-Gradient-Boost, Cat-Boost, Light-Gradient-Boost, Random Forest, and Extra-Trees. The proposed design was tested and checked using historical load data collected from Nagaland's Load Dispatch Unit in real-time. For the Random Forest and Extra-Tree Regressors, bagging methods yielded the lowest MAE values of 3.75 and 4.07, respectively. Boosting methods generated higher MAE and MAPE values than bagging methods and were thus unsuitable for real-time scenarios. Therefore, real-time use of bagging techniques such as Random forest and Extra-Trees is facilitated. Additionally, the mean absolute error and mean absolute percentage error study revealed that bagging methods outperform the other methods. The authors plan to continue their work by developing a generative adversarial network-based energy forecasting scheme based on domain information.
Author contribution All the authors participated and contributed equally in the analysis and interpretation of the results and data, drafting the article or revising it critically and preparing the final version.
Availability of data and material The data sets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Code availability
The custom code used in the current study is available from the corresponding author on reasonable request.

Conflict of interest
The authors declare no competing interests.