Forecasting the load of electrical power systems in mid‐ and long‐term horizons: a review

Electricity load forecasting is an important task for enhancing energy efficiency and operation reliability of the power system. Forecasting the hourly electricity load of the next day assists in optimizing the resources and minimizing the energy wastage. The main motivation of this study was to improve the robustness of short-term load forecasting (STLF) by utilizing long shortterm memory (LSTM) and genetic algorithm (GA). The proposed method is novel: LSTM networks are designed to avoid the problem of long-term dependencies, and GA is used to obtain the optimal LSTM's parameters, which are then applied to predict the hourly electricity load for the next day. The proposed method was trained using actual load and weather data, and the performance results showed that it yielded small mean absolute percentage error on the test data.With the advance of computing power, deep neural network (DNN) has gained much popularity and been applied to STLF in recent years [36]. LSTM is a special type of DNN that is suitable for time series prediction due to its capability of remembering both the short-term and the long-term behavior in time series data. In Reference [37], two types of LSTM were compared against other deep learning techniques for predicting electricity load of every hour or every minute. Their results showed that LSTM outperformed the other techniques. However, all of the neural network approaches above require setting up the initial weightings of the links in the network, but poorly chosen weightings could lead the searching process trapped in the local optimum. A hybrid of LSTM and GA is proposed in Section 4 to resolve this problem.Prior to presenting our approach in Section 4, this section gives a detailed description of LSTM. LSTM is an augmented recurrent neural network model. It learns sequential information with long term dependencies, and preserves information for a long period of time. Traditional recurrent neural network suffers from the vanishing gradient problem. That is, as the number of layers using the same activation function increases, the gradients of the loss function approaches zero, making it difficult to train the network through backpropagation of errors. To prevent the vanishing gradient problem, LSTM utilizes memory cells, where each cell maintains a cell state and a hidden cell state, and uses three gates (namely, input gate, output gate, and forget gate) to control the flow of information into or out of the cell. A formal explanation of the LSTM model is given below.LSTM is for time series modeling, which maps an input sequence x = {x 1 , x 2 , . . . , x n } to an output sequence y = y 1 , y 2 , . . . , y n . For the STLF problem under study, each x i represents the hourly electricity load and the weather data of day i, and each y i represents the hourly electricity load of day i + 1, indicating a look-ahead parameter of value one. The LSTM contains layers of memory cells, where the interaction between the LSTM layers is shown in Figure 1, and the architecture of a LSTM cell is shown...

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“…The problem of load forecasting has attracted much attention since the mid-1960s [21,22]. This section focuses the techniques for STLF.…”

Section: Related Work On Short-term Load Forecastingmentioning

confidence: 99%

Integrating Long Short-Term Memory and Genetic Algorithm for Short-Term Load Forecasting

Santra

Lin

2019

Energies

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“…Figure 3 illustrates the results from the forecasting model. forecasting stage to minimize the forecasting error [27][28][29].One of the most accurate techniques is WNN which has been used to predict the day-ahead load demand and weather conditions [30]. Figure 3 illustrates the results from the forecasting model.…”

Section: Weather and Load Forecastingmentioning

confidence: 99%

Control and EMS of a Grid-Connected Microgrid with Economical Analysis

2018

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Abstract:Recently, significant development has occurred in the field of microgrid and renewable energy systems (RESs). Integrating microgrids and renewable energy sources facilitates a sustainable energy future. This paper proposes a control algorithm and an optimal energy management system (EMS) for a grid-connected microgrid to minimize its operating cost. The microgrid includes photovoltaic (PV), wind turbine (WT), and energy storage systems (ESS). The interior search algorithm (ISA) optimization technique determines the optimal hour-by-hour scheduling for the microgrid system, while it meets the required load demand based on 24-h ahead forecast data. The control system consists of three stages: EMS, supervisory control and local control. EMS is responsible for providing the control system with the optimum day-ahead scheduling power flow between the microgrid (MG) sources, batteries, loads and the main grid based on an economic analysis. The supervisory control stage is responsible for compensating the mismatch between the scheduled power and the real microgrid power. In addition, this paper presents the local control design to regulate the local power, current and DC voltage of the microgrid. For verification, the proposed model was applied on a real case study in Oshawa (Ontario, Canada) with various load conditions.

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“…Technical research includes various studies on long‐term load forecasting models that use different variables that influence consumption, besides that of historical demand. () Phimphachanh et al presented a model of load forecasting using a recurrent neural network, mainly considering existing gross domestic product (GDP) data and the number of air conditioners available. Carmona et al applied an artificial neural network (ANN) model, taking into consideration only the historic demand model.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Notwithstanding the many studies on the subject, it should be noted that the development of forecasting models, which take into account the response of price demand of electricity, is still at an early stage of research. 1,2,19,20 In the present competitive electricity market, the system load can be significantly affected by prices. Since consumers can adjust their consumption behavior according to electricity market information, it is essential to consider electricity tariffs in forecasting long-term loads.…”

Section: Introductionmentioning

confidence: 99%

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Application of artificial neural networks and fuzzy logic to long-term load forecast considering the price elasticity of electricity demand

Miranda

Abaide

Sperandio

et al. 2018

Int Trans Electr Energ Syst

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Summary Over the past few decades, the behavior of electricity consumption has been changing, especially because of improvements in the distributed generation segment and technological innovations presented by smart grids. The use of microgeneration and the availability of electricity pricing in real time allow consumers to control their consumption, or generation, according to market conditions. This new dynamic tends to increasingly change the price elasticity of electricity demand, by indicating the need to readjust load forecasting models. In this market environment, in addition to providing robust estimates for the planning and operation of electric power systems, load forecasting models have become fundamental in the context of demand management. Thus, this paper proposes to develop an artificial neural network and fuzzy logic for load forecasting to perform an efficiency analysis. This system is able to provide estimates of the elasticity of electricity demand behavior with more satisfactory results. To do so, improvements in the neural network with multilayer perceptron are proposed. In this case, the adaptation of parameters to correlate variations in consumption with the changes in electricity tariffs was developed. The addition of this new structure produced better results compared with the conventional neural network. Computer tests were conducted using historical data from the ISO New England Inc and PJM Interconnection. Price elasticity estimates of electricity demand showed a sharp increase of demand in relation to the elasticity behavior.

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Forecasting the load of electrical power systems in mid‐ and long‐term horizons: a review

Cited by 122 publications

References 103 publications

Integrating Long Short-Term Memory and Genetic Algorithm for Short-Term Load Forecasting

Integrating Long Short-Term Memory and Genetic Algorithm for Short-Term Load Forecasting

Control and EMS of a Grid-Connected Microgrid with Economical Analysis

Application of artificial neural networks and fuzzy logic to long-term load forecast considering the price elasticity of electricity demand

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