Tokyo stocks took a downturn Wednesday following an overnight fallback on Wall Street and strengthening of the yen. The 225-issue Nikkei average gave up 106.63 points, or 0.49 percent, to end at 21,778.61. On Tuesday, the key market gauge rose 129.40 points. The Topix index of all issues listed on the first section of the Tokyo Stock Exchange finished down 6.71 points, or 0.42 percent, at 1,596.29, after advancing 15.20 points the previous day. The market opened sharply lower as investors rushed to sell after U.S. shares were battered by a substantial worsening of the Institute for Supply Management's manufacturing index for September, announced Tuesday.
Wall Street is closing out a solid quarter with a Friday of mixed trading. Major indexes were moving in a small range, with the Dow Jones industrials posting a small loss while other indexes were mostly higher. Investors weighed several corporate deals and new economic data on consumer spending and inflation. Energy stocks were down the most as the price of crude oil headed lower. KEEPING SCORE: The Dow Jones industrial average slid 40 points, or 0.2%, to 20,687 as of 8:30 a.m.
Most of the current game-theoretic demand-side management methods focus primarily on the scheduling of home appliances, and the related numerical experiments are analyzed under various scenarios to achieve the corresponding Nash-equilibrium (NE) and optimal results. However, not much work is conducted for academic or commercial buildings. The methods for optimizing academic-buildings are distinct from the optimal methods for home appliances. In my study, we address a novel methodology to control the operation of heating, ventilation, and air conditioning system (HVAC). With the development of Artificial Intelligence and computer technologies, reinforcement learning (RL) can be implemented in multiple realistic scenarios and help people to solve thousands of real-world problems. Reinforcement Learning, which is considered as the art of future AI, builds the bridge between agents and environments through Markov Decision Chain or Neural Network and has seldom been used in power system. The art of RL is that once the simulator for a specific environment is built, the algorithm can keep learning from the environment. Therefore, RL is capable of dealing with constantly changing simulator inputs such as power demand, the condition of power system and outdoor temperature, etc. Compared with the existing distribution power system planning mechanisms and the related game theoretical methodologies, our proposed algorithm can plan and optimize the hourly energy usage, and have the ability to corporate with even shorter time window if needed.
Investors braced for a rocky ride this week, uncertain whether the upturn in oil prices will last and fearful of what first-quarter earnings statements will show. The situation is compounded by uncertainty over when the U.S. Federal Reserve will impose -- or if it will impose -- its next interest rate hike. The U.S. equity markets were in the red last week, pressured by oil prices and a weak recovery, with the Nasdaq closing down 3.1 percent for the year. Oil futures advanced 6.6 percent Friday in New York, still trying to recover from February's 13-year low, following word U.S. output fell for the 10th time in 11 weeks, with the number of active oil rigs at their lowest level since 2009. But next Sunday's planned meeting among major oil producers in Doha, Qatar, to discuss freezing output throws yet another layer of uncertainty over the markets.