## 1 Introduction  
In the background of fossil fuel depletion and increasing global warming caused by high carbon dioxide density in the atmosphere, power production plants using renewable energy have attracted international attention. In the case of electric power supply in isolated islands of Japan, renewable energy power production plants will be installed in many isolated islands in the near future to replace the fuel-consuming diesel generators. This paper presents an optimal operation method for the DC (direct current) grid system (DC smart house) with PV (photovoltaic) and SC (solar collector), assuming that the installation location for the DC smart house is Okinawa. Okinawa has about 40 isolated islands located in Southwest Japan. These islands mostly depend on diesel generators for power supply due to the long distance to the mainland of Okinawa and their small areas. If it is possible to provide power load and heat load by PV with SC in the DC grid system, the consumers can then enjoy more advantages [1]. However, the solar radiation forecast errors were not considered in our previous work. Though some researchers have proposed a solar radiation forecasting method using weather prediction [2], the electrical loss of inverter and efficiency of battery charge/discharge were not considered. This paper presents a re-planning operation method using Tabu Search for DC smart house considering solar radiation forecast error, battery and inverter losses. Simulation results show that daily running costs are minimized by the proposed method on sunny and rainy days. Once the proposed methods become widely applied, they will be able to contribute to society as a global warming prevention technology by a positive introduction of renewable energy systems. It is well known that photovoltaic (PV) systems are capable of generating electricity in a clean, quiet and reliable way, but the power output fluctuation depends on weather conditions. From the point of view of energy storage, batteries are integrated with a PV system for providing energy to load during night time and sunless period. Nowadays, a new home energy management system (HEMS) with PV, windmill, and electrical storage system has been reported in [3]. Additionally, feed-in-tariffs [4] for residential PV systems are introduced in many countries. Furthermore, solar collector (SC) and heat pump (HP) systems are developed in the heat supply [5]. An advanced efficient utilization method of these renewable systems is important for utilizing solar energy [6]. Solar radiation forecasting [7] is an important tool for determining the amount of storage battery energy, PV power output, and heat thermal energy collection of SC. The power conversion system is usually alternate current (AC) in electric power companies. However, a direct current (DC) grid system [8] for residential housing has been the focus of recent years. In a typical residential house, power load and heat load are the most important factors. Since the DC grid has lower power loss than that of the AC grid system, usually a stand-alone photovoltaic power system is designed and implemented to operate residential DC power appliances such as lamps, heat load, _etc_. Recently, many related works about energy management for smart house have been reported. [PERSON] _et al._ introduced enhancements to an energy service decision-support tool that aims to aid households in making more intelligent decisions when operating their major home appliances due to maximizing the net benefits gained by the end users [9]. [PERSON] _et al._ presented an energy management system for a microgrid, including PV with embedded storage units and a gas microturbine [10]. The operational planning is based on the PV power output forecasting. A localcontrolling algorithm was proposed in [11] to control domestic electricity and heat demand. The algorithm of planning is based on the current state of the system and on the external condition on heat demand.