1 Introduction Load forecasting is a prediction of future electricity demand based on known demand, taking into account relevant factors such as politics, economy, and climate. Load forecasting includes two aspects: prediction of future demand (power) and prediction of future electricity consumption (energy). The prediction of electricity demand determines the size of the new capacity of the power generation, transmission, and distribution systems; the prediction of electricity consumption determines the type of power generation equipment (such as peak-shaving units, base-load units, etc.). The purpose of load forecasting is to provide the load development status and level, and at the same time determine the power supply and consumption of each power supply area, the maximum power supply and consumption load of each planned year, and the overall load development level of the planned area, and determine the composition of the power load for each planned year. [b]2 Methods and Characteristics of Load Forecasting[/b] 2.1 Unit Consumption Method The required electricity is determined according to the national product output, output value plan, and unit consumption of electricity. The unit consumption method is divided into two types: "product unit consumption method" and "output value unit consumption method". The key to predicting load using the "unit consumption method" is to determine the appropriate product unit consumption or output value unit consumption. From my country's actual situation, the general rule is that the unit consumption of products increases year by year, while the unit consumption of output value decreases year by year. The advantage of the unit consumption method is that it is simple and has a good effect on short-term load forecasting. The disadvantage is that it requires a lot of detailed research work, is relatively general, and is difficult to reflect the influence of modern economic, political, and climatic conditions. 2.2 Trend Extrapolation Method When the power load shows a certain upward or downward trend over time, and there is no obvious seasonal fluctuation, and a suitable function curve can be found to reflect this trend, a trend model y = f(t) can be established using time t as the independent variable and time series value y as the dependent variable. When there is reason to believe that this trend can extend into the future, the variable t is assigned the required value, and the future value of the time series at the corresponding time can be obtained. This is the trend extrapolation method. There are two assumptions for applying the trend extrapolation method: ① It is assumed that the load does not change abruptly; ② It is assumed that the factors that develop the load also determine the future development of the load, and the condition is that they remain unchanged or do not change much. Selecting a suitable trend model is an important part of applying the trend extrapolation method. The graphical recognition method and the difference method are two basic methods for selecting the trend model. Extrapolation methods include linear trend forecasting, logarithmic trend forecasting, quadratic curve trend forecasting, exponential curve trend forecasting, and growth curve trend forecasting. The advantage of trend extrapolation is that it only requires historical data and a relatively small amount of data. The disadvantage is that load fluctuations can cause significant errors. 2.3 Elasticity Coefficient Method: The elasticity coefficient is the ratio between the average growth rate of electricity consumption and GDP. The total electricity consumption at the end of the planning period is obtained by combining the GDP growth rate with the elasticity coefficient. The elasticity coefficient method determines the relative speed of electricity development with national economic development from a macroscopic perspective; it is an important parameter for measuring national economic development and electricity demand. The advantage of this method is its simplicity and ease of calculation. The disadvantage is that it requires extensive and detailed research. 2.4 Regression Analysis Method: Regression forecasting establishes a mathematical model based on historical load data. Regression analysis methods from mathematical statistics are used to statistically analyze the observed data of variables, thereby predicting future load. Regression models include univariate linear regression, multivariate linear regression, and nonlinear regression. Linear regression is used for medium-term load forecasting. Advantages: High prediction accuracy, suitable for medium- and short-term forecasting. Disadvantages: ① It is difficult to statistically analyze the total industrial and agricultural output value of the planning year in detail; ② Regression analysis can only calculate the development level of comprehensive electricity load, but cannot calculate the load development level of each power supply area, thus making it impossible to carry out specific power grid construction planning. 2.5 Time series method is to establish a mathematical model based on historical load data. This mathematical model describes the statistical regularity of the change process of the random variable of electricity load. On the other hand, it establishes a mathematical expression for load forecasting based on the mathematical model to predict future load. The time series method mainly includes autoregressive AR(p), moving average MA(q), and autoregressive and moving average ARMA(p,q). The advantages of these methods are: less historical data required and less workload. The disadvantages are: It does not consider the factors of load change, only focuses on data fitting, and is insufficient in handling regularity. It is only suitable for short-term forecasting where load changes are relatively uniform. 2.6 Grey model method Grey prediction is a method for predicting systems containing uncertain factors. Grey forecasting, based on grey system theory, can identify patterns that function within a specific period and establish load forecasting models when data is limited. It is divided into two types: ordinary grey system models and optimized grey models. Ordinary grey forecasting models are exponential growth models. When electricity load grows strictly according to an exponential law, this method has advantages such as high prediction accuracy, small sample data requirements, simple calculation, and verifiability. However, its disadvantage is that for fluctuating electricity loads, the prediction error is larger, which does not meet practical needs. Optimal grey models, on the other hand, can transform fluctuating original data sequences into sequences with enhanced regularity and exponential growth, greatly improving prediction accuracy and the applicability of the grey model method. Grey model methods are suitable for short-term load forecasting. Advantages of grey forecasting: requires limited load data, does not consider distribution patterns or trends, is computationally convenient, has high short-term prediction accuracy, and is easy to verify. Disadvantages: firstly, the greater the data dispersion (i.e., the greater the data gray level), the worse the prediction accuracy; secondly, it is not suitable for long-term forecasting of power systems, extending the forecast several years in advance. 2.7 Delphi Method The Delphi method is a method of judging and predicting the problem under study based on the direct experience of people with specialized knowledge; it is also called the expert survey method. The Delphi method is characterized by feedback, anonymity, and statistical properties. The advantages of the Delphi method are: ① It can speed up forecasting and save forecasting costs; ② It can obtain various different but valuable viewpoints and opinions; ③ It is suitable for long-term forecasting, especially when historical data is insufficient or there are many unpredictable factors. The disadvantages are: ① It may be unreliable for load forecasting by region; ② Expert opinions may sometimes be incomplete or impractical. 2.8 Expert System Method The expert system forecasting method analyzes hourly load and weather data stored in a database over the past few years or even decades, thereby gathering the knowledge of experienced load forecasters, extracting relevant rules, and making load forecasts according to certain rules. Practice has proven that accurate load forecasting not only requires the support of advanced technology but also the integration of human experience and wisdom. Therefore, technologies like expert systems are needed. The expert system method is a good way to transform unquantifiable human experience. However, expert system analysis is inherently a time-consuming process, and even knowing the impact of certain complex factors (such as weather factors) on load makes it difficult to accurately and quantitatively determine their impact on load-bearing areas. Expert system forecasting is suitable for medium- and long-term load forecasting. Its advantages are: ① It can gather the knowledge and experience of multiple experts, maximizing their capabilities; ② It possesses abundant data and information, and considers a comprehensive range of factors, which is conducive to arriving at more accurate conclusions. Its disadvantages are: ① It lacks self-learning ability and is limited by the total amount of knowledge stored in the database; ② It has poor adaptability to sudden events and constantly changing conditions. 2.9 Neural Network Method Neural network (ANN) forecasting technology can mimic the intelligent processing of the human brain and has adaptive capabilities for a large number of unstructured and non-deterministic patterns. ANN is more suitable for short-term load forecasting than for medium- and long-term load forecasting. This is because short-term load changes can be considered a stationary stochastic process. Long-term load forecasting, however, may be affected by major political and economic shifts that could disrupt the mathematical foundation of its model. Advantages: ① It can mimic the intelligent processing of the human brain; ② It has adaptive functions for a large number of unstructured and imprecise laws; ③ It has the characteristics of information memory, autonomous learning, knowledge reasoning, and optimized calculation. Disadvantages: ① The determination of initial values ​​cannot utilize existing system information, and it is easy to get trapped in local minima; ② The learning process of neural networks is usually slow, and its adaptability to sudden events is poor. 2.10 Optimal Combination Prediction Method Optimal combination has two meanings: one is to select appropriate weights from the results obtained from several prediction methods and perform a weighted average; the other is to compare several prediction methods and select the prediction model with the best fit or the smallest standard deviation for prediction. It should also be noted that the combined prediction method plays a role when a single prediction model cannot completely and accurately describe the changing law of the predicted quantity. A model that can fully reflect the actual development law may be better than the prediction effect of the combined prediction method. The advantages of this method are: it optimizes the combination of information from multiple single prediction models, and considers the influence information more comprehensively, thus effectively improving the prediction effect. Disadvantages are: ① It is difficult to determine the weights; ② It is impossible to include all factors that will play a role in the future in the model, which to some extent limits the improvement of prediction accuracy. 2.11 Wavelet Analysis Prediction Technology Wavelet analysis is a time-domain-frequency domain analysis method. It possesses excellent localization properties in both the time and frequency domains and can automatically adjust the sampling density according to the signal frequency. It easily captures and analyzes weak signals and arbitrary small parts of signals and images. Its advantages are: it can use progressively finer sampling steps for different frequency components, thus focusing on arbitrary details of the signal, especially sensitive to singular signals, and can handle weak or abrupt signals well. Its goal is to convert the information of a signal into wavelet coefficients, which can then be easily processed, stored, transmitted, analyzed, or used to reconstruct the original signal. These advantages determine that wavelet analysis can be effectively applied to the study of load forecasting problems. [b]3 Conclusion[/b] Load forecasting is a prerequisite for power system dispatching, real-time control, operation planning, and development planning. It is a fundamental piece of information that a power grid dispatching department and planning department must possess. Improving load forecasting technology is beneficial for planned electricity management, for rationally arranging grid operation modes and unit maintenance plans, for saving coal and oil and reducing power generation costs, for formulating reasonable power source construction plans, and for improving the economic and social benefits of the power system. Therefore, load forecasting has become an important aspect of modernizing power system management.