Deep Neural Network and Long Short-Term Memory for Electric Power Load Forecasting

Abstract

Forecasting domestic and foreign power demand is crucial for planning the operation and expansion of facilities. Power demand patterns are very complex owing to energy market deregulation. Therefore, developing an appropriate power forecasting model for an electrical grid is challenging. In particular, when consumers use power irregularly, the utility cannot accurately predict short- and long-term power consumption. Utilities that experience short- and long-term power demands cannot operate power supplies reliably; in worst-case scenarios, blackouts occur. Therefore, the utility must predict the power demands by analyzing the customers’ power consumption patterns for power supply stabilization. For this, a medium- and long-term power forecasting is proposed. The electricity demand forecast was divided into medium-term and long-term load forecast for customers with different power consumption patterns. Among various deep learning methods, deep neural networks (DNNs) and long short-term memory (LSTM) were employed for the time series prediction. The DNN and LSTM performances were compared to verify the proposed model. The two models were tested, and the results were examined with the accuracies of the six most commonly used evaluation measures in the medium- and long-term electric power load forecasting. The DNN outperformed the LSTM, regardless of the customer’s power pattern.

1. Introduction

Accurately predicting the power demand is very important for the stable operation of power systems with fluctuating power demands. The scale of the electric grid industry has grown with the shift of the social environment to a highly industrialized and information-oriented society. The amount of air-conditioning and heating equipment used by general consumers has rapidly increased. Accordingly, electricity consumption patterns change with changes in weather and specific days (e.g., regular holidays and temporary holidays). These changes require accurate and stable predictions, and an accurate analysis of the power load patterns is essential. The power load pattern has similar characteristics for all days of the week, but may vary for special days. For example, the power load pattern on a Wednesday is affected by the working day or a holiday on the previous day. Therefore, simply classifying the power load patterns by the day of the week is not appropriate; hence, prediction methods that can classify patterns according to power consumption data characteristics are necessary [1,2,3].

Power consumption patterns are periodically assessed by analyzing customer usage. These patterns are considered almost hourly, daily, weekly, monthly, and yearly. Electric power load forecasting is classified according to the duration of the planning period of the utility: short-term load forecasting (STLF), medium-term load forecasting (MTLF), and long-term load forecasting (LTLF) [4,5,6]. The LTLF predicts the power demand for more than one year and is applicable to power systems and long-term network planning. Essentially, two approaches are available for this purpose:

(1)

The peak load approach is used to find the trend curve obtained by plotting the past values of the annual peaks against the years of operation.

(2)

The energy approach aims to forecast annual sales, including annual energy sales, to different classes of customers (e.g., residential, commercial, industrial), which can then be converted to the annual peak demand using the annual load factors.

MTLF predicts the electricity demand for one month to one year, between the LTLF and STLF. Meanwhile, STLF estimates the power demand for one hour to one week. It has a scheduling function that determines the highest economic commitment of the power generation source. STLF also provides the system dispatcher with up-to-date weather forecasts to ensure that the system can be operated both economically and stably.

The electricity forecasting for building energy consumption is affected by weather (temperature, dew point, humidity, wind speed, wind direction, sky cover, and sunshine), time factors (the day of the week, the hour of the day holidays), and customer classes (residential, commercial, and industrial). Existing studies on building energy consumption can be divided into three categories [7,8]:

(1)

White box-based approaches, also named “physics-based models” require detailed physical information of complex buildings [9,10]. Owing to these characteristics, although the forecasting accuracy is high, a high computational cost is required for the simulation. Recently, there have been a series of attempts to simplify white box-based approaches. However, this simplification is prone to errors and generally overestimates the energy savings of buildings [11,12,13]. There are several tools, such as DOE-2, EnergyPlus, BLAST, TRYSYS, and ESP-r, which aid white box-based approach [14].

(2)

Black box-based approaches are also commonly referred to as “data-driven models”. These approaches rely on time-series statistical analyses and machine learning to assess and forecast electricity consumption [15,16,17]. Data-driven models are divided into three categories:

• Conventional models refer to exponential smoothing (ES) [18], moving average (MA) [19], statistical regressions [20], auto-regressive (AR) models [21], genetic algorithms (GA) [22], and fuzzy-based models [23,24]. They provide a good balance between forecasting accuracy and implementation simplicity. However, they have shown significant limitations in their ability to model nonlinear data patterns and the forecasting horizon.
• Classification-based models applied to electric power load forecasting are k-nearest neighbors (k-NN) [25] and decision trees (DT) [26]. Although both are intuitive models with high predictive accuracy, they are limited owing to their need for a comprehensive set of input data.
• Artificial intelligence models have been studied for many years and are generally referred to as machine learning and deep learning [27]. Some of the most popular artificial intelligence models are support vector machine (SVM) [28], artificial neural networks (ANN) [29], deep neural network (DNN) [30], and long short-term memory (LSTM) [31]. These models-based forecasting algorithms lead to less operator-dependent and more versatile methods in terms of data usage, with much higher forecasting accuracy.

(3)

Grey-box-based approaches have also been named “hybrid-based models” which are a combination of white box and black box models [32,33,34,35]. These models have the advantage of using improved single data-driven models with optimization, or a combination of several machine learning algorithms. However, these approaches have the shortcoming of computational inefficiency because these approaches involve uncertain inputs and complex interactions among elements and stochastic occupant behaviors [36,37,38].

The power consumption pattern may vary depending on the customer class as electric utilities usually serve different types of customers (i.e., residential, commercial, and industrial customers). The power consumption of residential buildings is low compared to those of commercial and industrial buildings and is not significantly affected by holidays, specific days, and seasons. The power consumption patterns in most industrial and commercial sectors are high on weekdays and low on holidays. However, some industrial buildings have irregular power consumption patterns, regardless of season and holiday. Industrial buildings with irregular power consumption cannot accurately predict medium- to long-term power forecasting. Consequently, the utility is unable to manage power supply in response to power demand. Therefore, in this study, two industrial buildings (i.e., companies T and B) with different power consumption patterns were selected to evaluate the medium- and long-term power forecasting performance accuracies of the proposed models. Company B is a two-shift manufacturer and Company T is a three-shift livestock processing firm. The two industrial buildings are located in Naju-si, Jeonnam, South Korea, and their electricity consumption data (per day) are collected monthly. The hourly power usage data comprised three years’ worth of data from 2017 to 2019. In addition, we measured the performance of the proposed models by adopting mean absolute error (MAE), root mean squared error (RMSE), coefficient of variation RMSE (CVRMSE), mean absolute error (MAPE), coefficient of determination (R²), and computation time.

The remainder of this study is organized as follows: Section 2 analyzes the power consumption of two buildings with different power consumption patterns and explains the deep learning techniques, DNN and LSTM. Section 3 describes the multivariate, DNN, and LSTM models proposed herein; Section 4 describes the test environment, including the test data set, and analyzes the test results; and Section 5 concludes the study and discusses future research.

2. Preliminaries and Problem Definition

2.1. Load Forecasting

Load forecasting predicts the power needed to meet short-, medium-, and long-term demands.

(1)

The advantages of load forecasting are as follows:

• It helps utility companies to better operate and manage supplies for their customers;
• It is an important process that can increase the efficiency and profit of power generation and distribution companies.
• It helps plan capacity and operation to provide a stable energy supply to all consumers [39,40,41]

(2)

The challenges of load forecasting are as follows:

• The power load series is complex and shows various seasonality levels; hence, a given time load can be accommodated at the same time in a specific weekday for the same time load and the previous week as well as the previous time load;
• Many important exogenous variables must be considered when forecasting power, especially those related to weather, making it difficult to achieve an accurate prediction [42,43].

Several load forecasting studies have analyzed weather factors and power consumption patterns in various ways [44,45,46,47,48]

The present study proposed that power demand forecasting includes special days (e.g., special holidays and official holidays) during the week and accounts for buildings with different power consumption patterns. In the former, the power consumption of on special day during the week is estimated to approximate that of the weekend.

Table 1. The holidays in Korea for three years.

Year	January	April	July	Autumn
2017	1/1(Tuesday), 1/27(Friday)–1/30(Monday)	X	X	10/1(Sunday)–10/9(Monday)
2018	1/1(Monday)	X	X	10/3(Wednesday), 10/9(Tuesday)
2019	1/1(Tuesday)	X	X	10/3(Thursday), 10/9(Wednesday)

X: No special days.

shows the dates and days of the week for a special day of January, April, July, and October in Korea in the last three years (2017–2019). For the latter, we forecast medium- and long-term power demands for the B and T companies with different power consumption patterns based on Table 1.

Figure 1 shows the average electricity usage for each day of the week for January (winter), April (spring), July (summer), and October (autumn) for company B for three years. The following observations are made from Figure 1:

(1)

The electricity usage of company B for three years is in the order of summer (121 MW) < spring (154 MW) < autumn (159 MW) < winter (197 MW). It is less affected by weather than summer and winter, where air-conditioning usage is the highest.

(2)

It showed the highest electricity usage occurs during working hours (8 a.m. to 7 p.m.) from Monday to Friday (weekdays). Saturdays and Sundays (weekends) show that there is almost no electricity usage (below 50 MW). In addition, there was almost no power consumption during lunchtime from 12:00 to 1:00 p.m. compared with other hours on weekdays.

(3)

As a result of comparing the electricity usage for special days of company B for three years, the power consumption pattern is regular in April and July because there are no special holidays. In addition, in October, as special holidays are mostly from Monday to Friday, power consumption patterns are regular, similar to those of April and July. However, January had high electricity usage on Wednesdays and Thursdays, excluding special holidays (Monday, Tuesday, and Friday).

Figure 2 shows the average electricity usage for each day of the week for January (winter), April (spring), July (summer), and October (autumn) for company T over three years. The following observations are made from Figure 2:

(1)

The electricity usage of T company for three years is in the order of winter (488 MW) < spring (511 MW) < autumn (546 MW) < summer (605 MW). As company T is a livestock meat processing company, cooling is more important than heating, so it uses more electricity in spring, autumn, and summer than in winter.

(2)

The power consumption pattern of company T’s weekend (Saturday to Sunday) is smaller than on weekdays, but fluctuations in electricity usage are more flexible at each time than those of company B. In particular, the power consumption pattern during the week largely fluctuates regardless of the time of day and day of the week.

(3)

As a result of comparing the electricity power for special days at company T for three years, it can be seen that the electricity usage of company T differs depending on the amount of supply, regardless of the special days.

In conclusion, we can see that the power consumption pattern of company T is different from that of company B, and the power consumption pattern depends on the amount of supply regardless of special days, time zones, days of the week, and seasons.

2.2. DNN

The multilayer perceptron (MLP) is a class of artificial neural network (ANN) that consists of at least three layers (input, hidden, and output layers) of nodes [49]. Except for input nodes, each node is a neuron that uses an activation function (i.e., step, sigmoid, tanh, ReLU: Rectified Linear Unit), which determines whether to output the received data to the next layer. The MLP is a machine learning solution and has been applied to various applications such as speech recognition, image recognition, and machine translation software. However, the MLP faces the problems of vanishing gradient, the reasoning of new facts, and the inability to process new data. These problems were solved by the development of deep learning [50].

DNNs have recently become a hot topic for image processing technology, reduced computational cost due to graphics processing unit development, and new machine learning techniques. DNNs have been actively studied to help solve the problem of complex and nonlinear functions. In addition, DNNs exhibit excellent learning performance on unclassified data; thus, they are used in various fields, such as artificial intelligence, graphic modeling, optimization, pattern recognition, and signal processing [51,52,53].

DNNs have a structure in which several hidden layers are added to an MLP, which has only one or two hidden layers. Figure 3 shows the DNN structure. The left side of the DNN has an input layer. The center has L − 1 hidden layers. The right side has an output layer. Hence, there are a total of L layers. $X={\left(1,x_1,x_2,\dots ,x_d\right)}^T$ is input to the input layer, and the output layer outputs $\mathrm{o}={\left(o_1,o_2,\dots ,o_c\right)}^T$. Therefore, there are $d$ nodes, except for the bias node in the input layer and c nodes in the output layer. The number of nodes, excluding the bias node, in layer l is denoted as $n_j$. The 0th layer corresponds to the input layer, and $n_0$ is equal to d. The Lth layer corresponds to the output layer, and $n_l$ is equal to c.

The weights connecting layers l − 1 and l are computed as $\left(n_{l-1}+1\right)\ast n_l$. The total weight may be represented by the matrix $U^l$ in Equation (1).

$$U^l=\left(\begin{array}{c}\begin{array}{cc}{u}_{10}^l& u_{11}^l\\ u_{20}^l& u_{21}^l\\ ⋮& ⋮\end{array} \begin{array}{cc}\dots & u_{1n_{l-1}}^l\\ \dots & u_{2n_{l-1}}^l\\ \ddots & ⋮\end{array}\\ u_{n_{l}0}^l{u}_{n_{l}1}^l\begin{array}{cc}\dots & u_{n_l{n}_l-1}^l\end{array}\end{array}\right),l=1,2,\dots ,L$$

(1)

2.3. LSTM

The algorithms most commonly used for time series forecasting among deep learning methods are the recurrent neural network (RNN) [54], LSTM [31], and gated recurrent unit (GRU) [55]. The RNN has many advantages concerning the processing of sequence data but has more disadvantages in the problems of long-term dependency, gradient disappearance, and gradient explosion. In 1997, Hochreiter proposed the LSTM to solve the long-term dependence problem of RNN [56]. Figure 4 shows the structure of LSTM, which consists of four gates: forget, input, update, and output gates.

The forget gate determines which information to delete, the input gate determines whether new information is stored in the cell state, the update gate updates the cell state, and the output gate determines which output value to output. Equation (2) is the calculation formula for each state, as follows:

$$\begin{gathered} f_t=\sigma (w_f\times \left[h_{t-1},x_t\right]+b_f), \\ {i}_t=\sigma (w_i\times \left[h_{t-1},x_t\right]+b_i), \\ {\tilde{C}}_t=tanh(w_c\times \left[h_{t-1},x_t\right]+b_c), \\ {C}_t=f_t\times C_{t-1}+i_t\times \widetilde{C_t}, \\ {o}_t=\sigma (w_o\times \left[h_{t-1},x_t\right]+b_o),\mathrm{and} \\ {h}_t=o_t\times \tanh \left(C_t\right), \end{gathered}$$

(2)

where $x_{t-1}$ and $x_t$ are the previous and current input values, respectively; $h_{t-1}$ and $h_t$ are the previous and current hidden gates, respectively; $C_{t-1}$ and $C_t$ are the previous and current cell states, respectively; $w_f$, $w_i$, $w_c$, and $w_o$ are the weight values connecting the input to the forget, input, update, and output gates, respectively; $b_f$, $b_i$, $b_c$, and $b_o$ are the bias values for the forget, input, update, and output gate’s calculation, respectively; $\sigma$ is a sigmoid function; and $tanh$ is a hyperbolic tangent function.

3. Proposed Electric Power Load Forecasting Methodology

Section 3 describes the proposed medium- to long-term power prediction methodology. Figure 5 shows the simulation flow chart proposed in this study. First, for the data reading process, the original data from years 2017 to 2019 are read for medium- to long-term power prediction. Second, the original data are divided into data partitioning processes for training and testing. Finally, after the proposed DNN and LSTM function are performed, they are evaluated using the prediction error calculation formula. Section 3.1 describes the multivariate model collected over three years (2017–2019) for medium- and long-term electric load forecasting. Section 3.2 and Section 3.3, respectively, describe the proposed DNN and LSTM, which are the most commonly used methods for time series prediction during deep learning.

3.1. Proposed Multivariate Model

Figure 6 shows the seasonal consumption patterns of the B and T companies over three years (2017–2019). The Korean weather has four distinct seasons with large differences in temperature over the course of the year, and features a large amount of rain in the summer. Considering the seasonal characteristics of Korea, we chose April in spring, July in summer, October in autumn, and January in winter. Figure 6a–d show the power loads of company B in spring, summer, autumn, and winter, respectively. Figure 6e–h illustrate the power loads of company T in spring, summer, autumn, and winter, respectively.

Figure 6a–d show that company B has similar power consumption patterns for three years because it is not significantly affected by season and time. However, in Figure 6e–h, the power consumption pattern is irregular because company T’s power consumption is independent of season and time.

A multivariate model was proposed, as shown in Figure 7, and was designed from the input to the output layer. The input layer has three layers for each year. An artificial neural network is defined as the first input layer (input_1: InputLayer), the second (input_2: InputLayer), and the third (input_3: InputLayer) which denote the power load in 2017, 2018, and 2019, respectively. The three input layers are each composed of one dense layer (dense_1: dense, dense_2: dense, dense_3: dense). The three dense layers concatenate to form one concatenate layer (concatenate_1: Concatenate). Finally, the dense layer (dense_4: Dense) is an input layer for use in the DNN.

Figure 8 shows a schematic of the proposed model, including the shape of the inputs and outputs of each layer. Keras’ sequential API (Application Programming Interface) was adopted to implement the DNN model [57]. Keras’ sequential API is a function that simply defines and uses complex models (e.g., sharing multiple layers or using various types of input and output). In Figure 8, the step size of the input and output of the InputLayer is set to three, Dense is set to three inputs, and output is set to 100 to have connectivity with the InputLayer. Three InputLayers are concatenated into one concatenate. Finally, the dense data are forecast as the final DNN structure. In future work, we will study whether forecasting performance varies depending on the step size.

3.2. Proposed DNN Model

The proposed DNN model comprises three layers (Figure 9), namely the input, hidden, and output layers. The input layer ($i_{t-1}$) is composed of one node. The hidden layer is composed of 100 nodes. The output layer ($o_t$) is composed of one node for load forecasting. As described in Section 3.1, the input layer ($i_{t-1}$) is the final dense layer with a value of dense_4: Dense. The proposed DNN was implemented using four hidden layers (H₁–H₄).

3.3. Proposed LSTM Model

Among the several deep learning technologies, LSTMs are widely used for time series prediction problems. Considering these observations, the LSTM network shown in Figure 10 was constructed for load forecasting. The input data (that is, $i_{t-1}$, $j_{t-1}$, and $k_{t-1})$ predicted the power using the load data for 3 years. $i_{t-1}$, $j_{t-1}$, and $k_{t-1}$ were the input data to be learned using the LSTM in the years 2017, 2018, and 2019, respectively.

3.4. Simulation Parameters of the DNN and LSTM

Table 2. Simulation parameters of the DNN and the LSTM.

Parameter	DNN	LSTM
Number of layers	4	2
Number of neurons	100	100
Number of epochs	10	10
Learning rate	0.05	0.05
Loss function	MSE	MSE
Optimizer	ADAM	ADAM
Weight initializer	1	1
Activation function	ReLU	ReLU

summarizes the simulation parameters used in this study, verifying that for the DNN and LSTM, the learning rate (=0.05), loss function (MSE: Mean Square Error), optimizer (ADAM) [58], and activation function (ReLU) [59] were the same.

4. Case Studies and Discussion

This section describes the test dataset and evaluation metrics for the two companies used for applying the proposed method. The experimental environment and results are also analyzed herein.

4.1. Test Environment and Test Data Set

To verify the proposed method, experiments were performed on a personal computer equipped with an Intel^® Xeon^® W-2133, 3.60 GHz CPU (Intel, Santa Clara, CA, USA), and 32 GB RAM. The test operating system was Windows 10 (64 bit) (Microsoft, Redmond, WA, USA). All the proposed methods were implemented using deep learning libraries provided by TensorFlow [60] and Keras [57].

The dataset used in this study selected two industrial buildings (i.e., Company T and B) with different power consumption patterns. The two industrial buildings are located in Naju, Jeollanam-do, Korea. Their electricity usage data (per day) are collected monthly at one-hour intervals, and hourly electricity usage data is composed of three-year data from years 2017 to 2019. For MTLF, as illustrated in Figure 5, data for the first three weeks of each month were used for training and tested for the last week. For the LTLF, data from January to September were used for training and the last three months were tested.

Table 3. Training and testing data for electric power load forecasting.

Term	Training Set		Testing Set
	From	To	From	To
MTLF
	1 January 2019	22 January 2019	23 January 2019	31 January 2019
	1 February 2019	20 February 2019	21 February 2019	29 February 2019
	1 March 2019	22 March 2019	23 March 2019	31 March 2019
	1 April 2019	21 April 2019	22 April 2019	30 April 2019
	1 May 2019	22 May 2019	23 May 2019	31 May 2019
	1 June 2019	21 June 2019	22 June 2019	30 June 2019
	1 July 2019	22 July 2019	23 July 2019	31 July 2019
	1 August 2019	22 August 2019	23 August 2019	31 August 2019
	1 September 2019	21 September 2019	22 September 2019	30 September 2019
	1 October 2019	22 October 2019	23 October 2019	31 October 2019
	1 November 2019	21 November 2019	22 November 2019	30 November 2019
	1 December 2019	22 Dec. 2019	23 Dec. 2019	31 December 2019
LTLF
	1 January 2019	13 September 2019	14 September 2019	31 December 2019

shows that the proposed methods for MTLF and LTLF were applied by dividing training data (70%) and test data (30%), respectively.

4.2. Performance Evaluation Metrics

Four widely used performance metrics (i.e., MAE, RMSE, CVRMSE [61], R², and MAPE) were adopted to assess the prediction accuracy of the proposed methods.

$$\mathrm{MAE}=\frac{1}{n}\times {\displaystyle \sum }_{i=1}^n\left(\left|y_i-{\widehat{y}}_i\right|\right)$$

(3)

$$\mathrm{RMSE}=\sqrt{\frac{1}{n}\times {\displaystyle \sum }_{i=1}^n{\left(y_i-{\widehat{y}}_i\right)}^2}$$

(4)

$$\mathrm{CVRMSE}=\frac{\mathrm{RMSE}}{{\overline{y}}_i}\times 100$$

(5)

$${\mathrm{R}}^2=1-\frac{\mathrm{SSE}}{\mathrm{SST}}=1-\frac{{\sum }_{i=1}^n{\left(y_i-{\overline{y}}_i\right)}^2}{{\sum }_{i=1}^n{\left(y_i-{\widehat{y}}_i\right)}^2}$$

(6)

$$\mathrm{MAPE}=\frac{100}{n}\times {\displaystyle \sum }_{i=1}^n\left|\frac{y_i-{\widehat{y}}_i}{y_i}\right|$$

(7)

In Equations (3)–(7), $y_i$ identifies the actual value of sample i, ${\widehat{y}}_i$ identifies the predicted value of sample i, n is the testing data, ${\overline{y}}_i$ indicates the mean of the predicted values, SSE denotes the residual sum of squares, and SST represents the total sum of squares.

Finally, the calculation time required to perform the computation process for the proposed method was also adopted to evaluate the proposed methods.

4.3. Comparison and Analysis of Medium-Term Electric Power Load Forecasting

Table 4. Comparison of the DNN and LSTM performances for medium-term electric power load forecasting.

Month	Metrics	Company B			Company T			Δ
		DNN (A)	LSTM (B)	ΔB (A–B)	DNN (C)	LSTM (D)	ΔT (C–D)	ΔDNN (A–C)	ΔLSTM (B–D)
January	MAE(MW)	0.15	0.43	−0.28	0.19	1.09	−0.90	−0.66	−0.04
	RMSE(MW)	0.30	0.97	−0.67	0.26	1.75	−1.49	−0.78	0.04
	CVRMSE(%)	0.73	2.37	−1.64	0.21	1.44	−1.23	0.93	0.52
	MAPE (%)	0.58	0.94	−0.36	0.16	0.88	−0.72	0.06	0.42
	R2	0.99	0.99	0	0.99	0.99	0	0	0
	Time (ms)	1.37	4.17	−2.80	1.64	4.28	−2.64	−0.11	−0.27
February	MAE(MW)	0.15	0.78	−0.63	0.13	1.02	−0.89	−0.24	0.02
	RMSE(MW)	0.35	2.37	−2.02	0.28	1.67	−1.39	0.70	0.07
	CVRMSE(%)	0.81	5.49	−4.68	0.23	1.37	−1.14	4.12	0.58
	MAPE (%)	0.67	2.49	−1.82	0.10	0.77	−0.67	1.72	0.57
	R2	0.99	0.99	0	0.99	0.99	0	0	0
	Time (ms)	1.34	3.73	−2.39	1.51	4.43	−2.92	−0.70	−0.17
March	MAE(MW)	0.20	0.80	−0.60	0.21	1.47	−1.26	−0.67	−0.01
	RMSE(MW)	0.49	1.79	−1.3	0.40	2.41	−2.01	−0.62	0.09
	CVRMSE(%)	1.52	5.54	−4.02	0.33	1.99	−1.66	3.55	1.19
	MAPE (%)	1.65	4.40	−2.75	0.16	1.25	−1.09	3.15	1.49
	R2	0.99	0.99	0	0.99	0.99	0	0	0
	Time (ms)	1.35	4.09	−2.74	1.74	4.81	−3.07	−0.72	−0.39
April	MAE(MW)	0.28	1.56	−1.28	0.14	0.88	−0.74	0.68	0.14
	RMSE(MW)	0.96	6.08	−5.12	0.19	1.29	−1.10	4.79	0.77
	CVRMSE(%)	3.02	19.14	−16.12	0.15	1.01	−0.86	18.13	2.87
	MAPE (%)	2.15	8.75	−6.60	0.10	0.67	−0.57	8.08	2.05
	R2	0.99	0.97	0.02	0.99	0.99	0	−0.02	0
	Time (ms)	1.31	3.95	−2.64	1.60	4.28	−2.68	−0.33	−0.29
May	MAE(MW)	0.10	0.87	−0.77	0.20	6.60	−6.40	−5.73	−0.10
	RMSE(MW)	0.29	2.77	−2.48	0.27	9.27	−9.00	−6.50	0.02
	CVRMSE(%)	1.05	10.05	−9.00	0.17	5.68	−5.51	4.37	0.88
	MAPE (%)	2.13	8.05	−5.92	0.12	4.00	−3.88	4.05	2.01
	R2	0.99	0.99	0	0.99	0.88	0.11	0.11	0
	Time (ms)	1.42	4.12	−2.70	1.74	4.62	−2.88	−0.50	−0.32
June	MAE(MW)	0.17	0.78	−0.61	0.55	1.41	−0.86	−0.63	−0.38
	RMSE(MW)	0.32	1.52	−1.20	0.69	2.02	−1.33	−0.50	−0.37
	CVRMSE(%)	1.10	5.21	−4.11	0.42	1.22	−0.8	3.99	0.68
	MAPE (%)	1.90	8.38	−6.48	0.34	0.82	−0.48	7.56	1.56
	R2	0.99	0.99	0	0.99	0.99	0	0	0
	Time (ms)	1.36	4.28	−2.92	1.62	4.43	−2.81	−0.15	−0.26
July	MAE(MW)	0.09	0.34	−0.25	0.40	1.70	−1.30	−1.36	−0.31
	RMSE(MW)	0.19	0.62	−0.43	0.53	2.53	−2.00	−1.91	−0.34
	CVRMSE(%)	0.87	2.84	−1.97	0.34	1.61	−1.27	1.23	0.53
	MAPE (%)	1.12	4.04	−2.92	0.25	1.07	−0.82	2.97	0.87
	R2	0.99	0.99	0	0.99	0.99	0	0	0
	Time (ms)	1.43	4.17	−2.74	1.65	4.63	−2.98	−0.46	−0.22
August	MAE(MW)	0.19	0.74	−0.55	0.35	1.44	−1.09	−0.70	−0.16
	RMSE(MW)	0.43	1.92	−1.49	0.48	2.47	−1.99	−0.55	−0.55
	CVRMSE(%)	2.34	10.46	−8.12	0.31	1.61	−1.3	8.85	2.03
	MAPE (%)	3.42	6.27	−2.85	0.22	0.94	−0.72	5.33	3.20
	R2	0.99	0.99	0	0.99	0.99	0	0	0
	Time (ms)	1.37	4.75	−3.38	1.67	4.71	−3.04	0.04	−0.30
September	MAE(MW)	0.10	0.77	−0.67	0.27	2.97	−2.67	−2.17	−0.17
	RMSE(MW)	0.21	2.46	−2.25	0.76	4.90	−4.14	−2.44	−0.55
	CVRMSE(%)	0.72	8.45	−7.73	4.16	0.65	3.51	7.80	−3.44
	MAPE (%)	1.12	3.76	−2.64	0.24	3.17	−2.93	0.59	0.88
	R2	0.99	0.99	0	0.99	0.95	0.04	0.04	0
	Time (ms)	1.30	3.98	−2.68	1.59	4.60	−3.01	−0.62	−0.29
October	MAE(MW)	0.13	0.84	−0.71	0.27	1.41	−1.41	−0.57	−0.14
	RMSE(MW)	0.24	1.39	−1.15	0.43	2.20	−1.77	−0.81	−0.19
	CVRMSE(%)	0.83	4.80	−3.97	0.34	1.76	−1.42	3.04	0.49
	MAPE (%)	1.01	6.81	−5.80	0.22	1.17	−0.95	5.64	0.79
	R2	0.99	0.99	0	0.99	0.99	0	0	0
	Time (ms)	1.44	4.14	−2.70	1.78	4.95	−3.17	−0.81	−0.34
November	MAE(MW)	0.10	0.30	−0.20	0.14	1.76	1.62	−1.46	−0.04
	RMSE(MW)	0.18	0.44	−0.26	0.31	2.40	−2.09	−1.96	−0.13
	CVRMSE(%)	0.88	2.15	−1.27	0.25	1.97	−1.72	0.18	0.63
	MAPE (%)	0.82	2.40	−1.58	0.12	1.55	−1.43	0.85	0.70
	R2	0.99	0.99	0	0.99	0.99	0	0	0
	Time (ms)	1.35	3.92	−2.57	1.63	4.49	−2.86	−0.57	−0.28
December	MAE(MW)	0.09	0.22	−0.13	0.16	1.29	−1.13	−1.07	−0.07
	RMSE(MW)	0.14	0.45	−0.31	0.35	2.00	−1.65	−1.55	−0.21
	CVRMSE(%)	0.52	1.68	−1.16	0.31	1.80	−1.49	−0.12	0.21
	MAPE (%)	0.44	0.84	−0.40	0.17	1.36	−1.19	−0.52	0.27
	R2	0.99	0.99	0	0.99	0.99	0	0	0
	Time (ms)	1.39	4.16	−2.77	1.76	4.67	−2.91	−0.51	−0.37
Average	MAE(MW)	0.15	0.70	−0.55	0.25	1.92	−1.67	−0.10	−2.62
	RMSE(MW)	0.34	1.90	−1.56	0.41	2.91	−2.50	−0.07	−4.81
	CVRMSE(%)	1.20	6.52	−5.32	0.60	1.84	−1.24	0.60	−8.36
	MAPE (%)	1.42	4.76	−3.34	0.18	1.47	−1.29	1.24	−6.23
	R2	0.99	0.99	0	0.99	0.99	0	0	0
	Time (ms)	1.37	4.12	−2.75	1.66	4.58	−2.92	−0.29	−8.70

shows the comparison of the proposed DNN and LSTM for medium-term electric power load forecasting. Lower values of MAE (MW), RMSE (MW), CVRMSE (%), and MAPE (%) denote higher model accuracy. The R² values approaching one and the score values of approximately 100% show that the predictive value approximates the actual value. To compare the performances of the proposed DNN and LSTM, the difference between the DNN and LSTM of company B with similar power consumption patterns is expressed by Δ_B, and the difference between the DNN and LSTM of company T having irregular power consumption patterns is expressed by Δ_T. Meanwhile, to compare the performances of companies B and T with irregular power consumption patterns, the differences between the proposed DNN and LSTM are represented by Δ_DNN and Δ_LSTM, respectively.

In the medium-term power prediction, the MAE, RMSE, CVRMSE (%), and MAPE of company B with a constant power consumption pattern indicated better performance than the proposed LSTM, with an average DNN of 0.55 MW, 1.56 MW, 5.32%, and 3.34%, respectively. The calculation time for company B was better than the proposed LSTM by an average of 2.75 ms. The R² for company B was approached 0.99 for the proposed DNN and LSTM, except for the proposed LSTM for April (R² = 0.97). The MAE, RMSE, CVRMSE (%), and MAPE of company T with irregular power consumption patterns outperformed the proposed LSTM with an average of 1.67 MW, 2.5 MW, 1.24% and 1.29%, respectively. The calculation time of company T was better than the proposed LSTM by 2.92 ms on average. The R² for company T approached 0.99 for the proposed DNN and LSTM, except for the LSTM of the months of May (R² = 0.88) and September (R² = 0.95).

The comparison results of MAE, RMSE, CVRMSE (%), MAPE, R², and calculation time for the proposed DNN and LSTM showed that the DNN and LSTM of company B with a better power consumption pattern outperformed those of company T with an irregular power consumption pattern. The comparison of MAE, RMSE, CVRMSE (%), MAPE, R², and calculation time of the proposed DNN illustrated that company B outperformed company T by 0.1 MW, 0.07 MW, 0.6%, 1.24%, 0, and 0.29 ms, respectively. Meanwhile, the comparison results of MAE, RMSE, CVRMSE (%), MAPE, R², and calculation time for the proposed LSTM showed that company B outperformed company T by 2.62 MW, 4.81 MW, 8.36%, 6.23%, 1.97, and 8.7 ms, respectively. In addition, Δ is the difference between Δ_DNN and Δ_LSTM. The MAE, RMSE, CVRMSE (%), MAPE, R², and calculation time of Δ_DNN’s were better than those of Δ_LSTM by −2.22 MW, −4.06 MW, −6.56%, −4.63%, 0.01, and −5.67 ms, respectively.

In conclusion, on average, the DNN showed a better performance in terms of prediction error and calculation time compared to the LSTM, regardless of the power consumption pattern. The DNN, of the company with a regular power consumption pattern exhibited the best performance.

Figure 11 shows the error between the actual and predicted values for the proposed DNN and LSTM for company B in April 2019. April in Korea is the representative month of spring. Figure 11a shows the error between the actual and predicted values using the proposed DNN. Figure 11b depicts the error between the actual and predicted values using the proposed LSTM. The error range in Figure 11a is within −8 to 8, while that in Figure 11b is −10 to 70. The proposed DNN method was superior to the proposed LSTM (Table 4).

Figure 12 shows the error between the actual and predicted values of the DNN and the LSTM proposed for company T in May 2019. In Korea, May is also the representative month of spring. Figure 12a shows the error between the actual and predicted values using the proposed DNN. Figure 12b depicts the error between the actual and predicted values using the proposed LSTM. The error range in Figure 12a is −2 to 2, while that in Figure 12b is −15 to 35. The proposed DNN method had an excellent performance because the error range was much smaller than that of the proposed LSTM.

4.4. Comparison and Analysis of Long-Term Electric Power Load Forecasting

Table 5. Comparison of the DNN and LSTM performances for long-term electric power load forecasting.

Metrics	Company B			Company T			Δ
	DNN (A)	LSTM (B)	ΔB (A–B)	DNN (C)	LSTM (D)	ΔT (C–D)	ΔDNN (A–C)	ΔLSTM (B–D)
MAE (MW)	0.02	0.05	−0.03	0.07	0.12	−0.05	−0.05	−0.07
RMSE (MW)	0.04	0.08	−0.04	0.08	0.29	−0.21	−0.04	−0.21
CVRMSE (%)	0.15	0.30	−0.15	0.06	0.23	−0.17	−0.09	0.07
MAPE (%)	0.21	0.48	−0.27	0.06	0.13	−0.07	0.15	0.35
R2	0.99	0.99	0	0.99	0.99	0	0	0
Time (ms)	11.98	41.78	−29.8	12.82	42.63	−29.81	−0.84	−0.85

shows the proposed DNN and LSTM comparisons for long-term power load forecasting. For the prediction error, the MAE (MW), RMSE (MW), CVRMSE (%), MAPE (%), R², and calculation time (ms) were adopted for the long-term electric power load forecasting. For long-term electric power forecasting, the MAE, RMSE, CVRMSE (%), and MAPE of company B (Δ_B) with a constant power consumption pattern indicated better performance than the proposed LSTM, with values of 0.03 MW, 0.04 MW, 0.15%, and 0.27%, respectively. The calculation time for company B was better than that of the proposed LSTM by an average of 29.8 ms. The R² for company B implied that the DNN and LSTM approached approximately 0.99. The MAE, RMSE, CVRMSE (%), and MAPE of company T (Δ_T) with irregular power consumption patterns showed a better performance than the proposed LSTM, with the proposed DNN being 0.05 MW, 0.21 MW, 0.16%, and 0.07%, respectively. The calculation time of company T was better than that for the proposed LSTM. The proposed DNN was 29.81 ms on average. The R² of company T for DNN and LSTM was approached approximately 0.99. The comparison results of MAE, RMSE, CVRMSE (%), MAPE, R², and calculation time for the proposed DNN and LSTM illustrated that the DNN and LSTM of company B (better power consumption pattern) outperformed that of company T (an irregular power consumption pattern). The comparison of the MAE, RMSE, CVRMSE (%), MAPE, R², and the calculation time of the proposed DNN (Δ_DNN) indicated that company B outperformed company T by 0.05 MW, 0.04 MW, 0.08%, 0.15%, 0, and 0.84 ms, respectively. Moreover, the comparison of the MAE, RMSE, CVRMSE (%), MAPE, R², and calculation time of the proposed LSTM (Δ_LSTM) showed that company B outperformed company T by 0.07 MW, 0.21 MW, 0.06%, 0.35%, 0, and 0.85 ms, respectively. In addition, MAE, RMSE, CVRMSE (%), MAPE, R², and calculation time of Δ_DNN were higher than those of Δ_LSTM by 0.08 MW, 0.25 MW, 0.31%, 0.34%, 0 and 59.61 ms, respectively.

Figure 13a,b show the errors between the actual and predicted values of the proposed DNN and LSTM, respectively, for company B in 2019. The error range in Figure 13a is from −0.4 to 0.5, while that in Figure 13b is from −1 to 0.8. As described in Table 4, the proposed DNN method outperformed the proposed LSTM.

Figure 14a,b show the errors between the actual and predicted values of the proposed DNN and LSTM, respectively, for company T in 2019. The error range in Figure 14a is from −0.8 to 1, while that in Figure 14b is from −5 to 3.

5. Conclusions and Future Work

Power demand forecasting is an essential process for planning periodic operations and facility expansion in the power sector. The electricity demand pattern is very complex because of energy market deregulation. Electric utilities employ electric power load forecasting to determine future inventory, costs, capacities, and interest rate changes. Therefore, finding a suitable prediction model for a specific power network is not an easy task.

In this study, two companies with different power consumption patterns were selected. Medium- and long-term power forecasting was predicted using a DNN and LSTM during deep learning. The experimental results showed that the proposed DNN outperformed the LSTM, regardless of the power consumption pattern. Furthermore, the performance of the proposed DNN was better than that of the proposed LSTM in terms of the prediction error (MAE, RMSE, CVRMSE, MAPE, and R²) and the calculation time.

However, the data used in this study have a limitation in that it does not consider weather data related to seasonality. Therefore, future research will expand medium- to long-term electric power forecasting by adding weather data to consider seasonality. In addition, our proposed method will be compared and evaluated against other methods for deep learning (GRU, Convolution-LSTM, Convolution Neural Network–LSTM, encoder-decoder LSTM) and machine learning (Adaptive Neuro Fuzzy Inference System-Subtractive Clustering, Adaptive Neuro Fuzzy Inference System-Fuzzy Clustering Means).