Due to the development of photovoltaic (PV) technology and the support from governments across the world, the conversion efficiency of solar energy has been improved. However, the PV power output is influenced by environment factors, resulting in features of randomness and intermittency. These features may have a negative influence on power systems. As a result, accurate and timely power prediction data is necessary for power grids to absorb solar energy. In this paper, we propose a new PV power prediction model based on the Gradient Boost Decision Tree (GBDT), which ensembles several binary trees by the gradient boosting ensemble method. The Gradient Boost method builds a strong learner by combining weak learners through iterative methods and the Decision Tree is a basic classification and regression method. As an ensemble machine learning algorithm, the Gradient Boost Decision Tree algorithm can offer higher forecast accuracy than one single learning algorithm. So GBDT is of value in both theoretical research and actual practice in the field of photovoltaic power prediction. The prediction model based on GBDT uses historical weather data and PV power output data to iteratively train the model, which is used to predict the future PV power output based on weather forecast data. Simulation results show that the proposed model based on GBDT has advantages of strong model interpretation, high accuracy, and stable error performance, and thus is of great significance for supporting the secure, stable and economic operation of power systems. and loads, especially PV power, can be managed in a more active way. For example, power system dispatch centers can arrange dispatch plans more reasonably and make adjustments more timely [6]. Moreover, smart grids can control a variety of power and reduce the capacity and operating costs of energy storage [7,8].According to input variables, PV prediction methods are able to be divided into two classes, namely, direct prediction and indirect prediction. Direct prediction methods only need historical power data, which is based on time series characteristics. Auto-Regressive and Moving Average Model (ARMA) and Autoregressive Integrated Moving Average Model (ARIMA) are the typical time-series prediction methods. In contrast, indirect prediction methods involve wider input data such as solar radiation, temperature, and other meteorological information provided by numerical weather prediction (NWP) systems. PV power output is closely related to the meteorological factors, and thus indirect prediction methods are generally more accurate and widely used. According to the algorithms used, PV prediction methods can be divided into physical methods and statistical methods. Generally, physical methods firstly predict the factors that directly influence PV power output and then obtain the PV output power by using the forecast values of the factors as the input of the physical model. On the other hand, statistical methods use historical data to build a statistical model based on some machine le...

Due to the continual fusion reaction, the sun generates tremendous energy. This solar energy is freely available and can be extracted by installing a large-scale solar power plant. Therefore, such PV solar plants are key contributors to cutting the energy deficit in remote areas. This study focused on predicting a 10-year performance analysis of a large-scale solar power plant by using 1 year of real-time data from the Quaid-e-Azam Solar Park (QASP) situated in Bahawalpur, Pakistan. For the purpose of prediction, the ARIMA model was developed using Python, which is one of the best tools in machine learning. Since ARIMA is a statistical technique for prediction, by using the developed model through Python, we predicted the values of the performance ratio (PR), production amount (MWh), and plan of array (POA) of the solar plant for the next 10 years using 1 year of real-time data. This machine learning prediction technique is very effective and efficient, compared with other traditional prediction and forecasting techniques, for estimating the performance of the solar power plant and the status of the solar power plant in the long-term future. The forecasting/prediction results acquired from the process show that power production during the next ten years increases to approximately 400 MW and that POA will grow from 6.8 to 7.8 W/m2. However, a decline occurred in the performance ratio, which decreased from 76.7% to 73%. Based on these results, the ARIMA model for predicting solar power generation is effective and can be utilized for any solar power plant.

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With the implementation of electric energy alternatives, the large-scale application of electric energy substitution represented by air-source heat pumps has replaced traditional coal-fired heating, which is beneficial for the environment and alleviates air pollution. However, the large-scale application of airsource heat pumps has brought power quality problems such as voltage sags, harmonic pollution, and three-phase imbalance to the distribution network. This paper studies the fixed-frequency and variablefrequency air-source heat pump, introduces its working principle, analyzes the mechanism of its power quality problem. Moreover, the paper establishes a simulation model for the fixed-frequency heat pump and variable-frequency heat pump to connect to the distribution network. This research mainly studies the impact of large-scale fixed-frequency heat pumps on the depth of voltage sags in the distribution network and the impact of large-scale variable-frequency heat pumps on the harmonic content of the distribution network under different penetration rates and uses measured data to verify the reliability of the simulation results. This paper uses experimental data for the first time to verify the real power quality problems of large-scale heat pumps, which can provide a reference for determining the power quality standards for heat pumps connected to the power grid. At the same time, it can also provide a reference for the power quality management of the distribution network that is actually connected to electric heating.