The utilization of solar power is one of the most effective approaches to achieve the goal of energy conservation and emission reduction for industrial areas. However, the temperature of the collected heat in a typical non-concentrating solar power collector is normally lower than 90 degrees Celsius, which indeed dissatisfies the demand of the heat required in this industry. Thus, in order to obtain an efficient source of heat, the condensed solar power heating technology will be put into practice in accordance with the features of low energy density and intermittence corresponding to solar power. In this paper, we firstly validate the correctness of the mathematical model of the efficient solar power heating system based on lenticular condensation. Thereafter, the practical design based on the verified mathematical model can be produced. Finally, the simulated results have been analyzed in detail and the assets of this design can thus be revealed.

The power demand over the electrical power system and smart grid is a random function in the time domain which is affected by a larger number of stochastic factors, for example weather, date and economy as well as a series of unpredictable human factors. Therefore, the most convenient and efficient methodology to forecast the power demand is a stochastic model based on statistics and fuzzy mathematics, because it can merge all complex factors which are difficult or even impossible to be modelled mathematically into an appropriate correction variable. In this paper, we will introduce a hybrid forecasting model of power demand which separates the forecasting process into three stages, i.e. long-term, middle-term and short-term analysis. Most of the long-term factors will be combined in a comprehensive correction factor for the middle-term stage. In the middle-term stage the forecasting mechanism integrates several different forecasting principles and methods to produce a combined forecasting result and dynamically adjusts its forecasting scheme by different weights for different forecasting methods by measuring and comparing the forecasting result and its corresponding practical measurement. By this self-adapting algorithm, the forecasting model is able to forecast the next 24hour power demand via using the historical data obtained in its database. In the short-term stage, a fine adjustment mechanism will be involved to enhance the reliability and robustness of the holistic forecasting mechanism.